The Chatbot Conference is only 3 weeks away, and right now, we are having our last sale of the year!
You can save up to $250 on tickets.
And it gets better. We just launched our Points Program and a Community, so every dollar you spend on tickets earns you a point you can use towards future Conferences, our Certified Workshops, and Membership Packages.
Activation does it means activating your car with a click ( if it has that ,of course) , well the same concept but in terms of neurons , neuron as in human brain ? , again close enough, neuron but in Artificial Neural Network (ANN).
The activation function decides whether a neuron should be activated or not.
A biological neuron in the human brain
If you have seen an ANN, which I sincerely hope you do you have seen they are linear in nature, so to use non — linearity in them we use activation functions and generate output from input values fed into the network.
A sample ANN network
Activation functions can be divided into three types
Linear Activation Function
Binary Step Activation Function
Non — linear Activation Functions
Linear Activation Function
It is proportional to the output values, it just adds the weighted total to the output. It ranges from (-∞ to ∞).
Graph showing linear activation function
Mathematically, the same can be written as
Equation of Linear Activation
Implementation of the same in Keras is shown below,
Binary Step Activation Function
It has a specific threshold value that determines whether a neuron should be activated or not.
Binary Step Activation Function Graph
Mathematically, this is the equation of the function
Equation of Binary Step Activation Function
Implementation of the same is not present in Keras so a custom function is made using TensorFlow as follows
Non — Linear Activation Functions
It allows ANN to adapt according to a variety of data and differentiate between the outputs. It allows the stacking of multiple layers since the output is a combination of input passed through multiple layers of the neural network.
Various non — linear activation functions are discussed below
Sigmoid Activation Function
This function accepts the input (number) and returns a number between 0 and 1. It is mainly used in binary classification as the output ranges between 0 and 1 e.g. you train a dog and cat classifier , regardless of how furry that dog is it classifies it as a dog not cat , there is no between , sigmoid is perfect for it.
Graph of Sigmoid function
Mathematically, the equation looks like this
Sigmoid Function Equation
Implementation of the same in Keras is shown below,
TanH Activation Function
This activation function maps the value into the range [ -1 , 1 ]. The output is zero centered , it helps in mapping the negative input values into strongly negative and zero values to absolute zero.
Comparison of tanh with sigmoid
Mathematically, the equation looks like this
Equation of tanh
Implementation of the same in Keras is shown below
ReLU ( Rectified Linear Unit)
It is one of the most commonly used activation functions, it solved the problem of vanishing gradient as the maximum value of the function is one. The range of ReLU is [ 0 , ∞ ].
Graph comparing Sigmoid and ReLU
Mathematically, the equation looks like this
Equation of ReLU
Implementation of the same in Keras is shown below,
Leaky ReLU
Upgraded version of ReLU like Covid variants .. sensitive topic …ok fine .. getting back to Leaky ReLU , it is upgraded as it solves the dying ReLU problem , as it has small positive slope in negative area.
Comparison of ReLU (left) and Leaky ReLU (right)
Mathematically, the equation looks like this
Implementation in Keras is coming right below
SoftMax Activation Function
Its a combination of lets guess .. is it tanh , hmm not quite , ReLU ? no or its leaky counterpart .. mhh not quite …. ok lets reveal it .. it is a combination of many sigmoid. It determines relative probability.
In Multiclass classification , it is the most commonly used for the last layer of the classifier. It gives the probability of the current class with respect to others.
Example of Softmax function
Mathematically, the equation looks like this
Equation of Softmax function
Implementation in Keras is given below
The whole notebook containing all codes used above
Loss functions are what make ANN (Artificial Neural Network) understand what is going wrong and how to get to that golden accuracy range, just like loss makes you cherish the profit and identify what went wrong.
Cause profit will come after right?.. RIGHT??
Now coming to the mathematical definition of the loss function, it is used to measure the inconsistency between the predicted value (^y) and the actual label (y). It is a non-negative value, where the robustness of the model increases along with the decrease of the value of the loss function.
It’s straightforward actually, it’s basically how well your algorithm models the dataset, if its prediction is totally off, your loss function will output a higher number. If it is pretty good, it will output a lower number.
In this article, they are divided into three types
Classification Losses
Regression Losses
Model Specific Losses
Let’s discuss them in-depth,
Classification Losses
In this, there are again various categories,
Binary Cross Entropy Loss / Log Loss
This loss function is mainly used in the training of a binary classifier, it compares each of the predicted probabilities to the actual class output which can be either 0 or 1.
It then calculates the score that penalizes the probabilities based on the distance from the expected value.
The mathematics behind it is discussed below,
That means how close or far from the actual value.
Equation of Log Loss
Here, pi is the probability of class 1, and (1-pi) is the probability of class 0.
When the observation belongs to class 1 the first part of the formula becomes active and the second part vanishes and vice versa in the case observation’s actual class are 0.
Implementation in Keras is shown below
Hinge Loss
It is used for classification problems and an alternative to cross — entropy, being primarily developed for support vector machines (SVM), difference between the hinge loss and the cross entropy loss is that the former arises from trying to maximize the margin between our decision boundary and data points.
Mathematically the equation is shown below,
Equation of Hinge Loss
Implementation in Keras is below,
Categorical Cross Entropy
It is a SoftMax activation plus a Cross-Entropy loss. If we use this loss, we will train an image classifier using CNN to predict for each class present for example which bird class a bird image classifier belongs to .
It is used for multi-class classification.
Since activation functions are not discussed in this article and if you don’t know about it I i got you covered I have one more article covering activation functions in Deep Learning using Keras link to the same is below…
Sparse CCEL(Categorical Cross Entropy Loss) and CCEL both compute categorical cross — entropy, the only difference being how the targets/labels should be encoded.
When using Sparse Categorical Cross entropy the targets are represented by the index of the category (starting from 0).
Your outputs have a shape of 4×2, which means you have two categories. Therefore, the targets should be a 4 dimensional vector with entries that are either 0 or 1. This is in contrast to Categorical Cross entropy where the labels should be one-hot encoded.
Mathematically, the equation is
Equation of Sparse Cross Entropy Loss
When to use which,
When to use categorical cross entropy and when to use sparse
Implementation in Keras is shown below,
Kullback — Leibler Divergence Loss
Simply put, it is the measure of how a probability distribution differs from another probability distribution.
Mathematically, it can be explained using
KL divergence between two distributions P and Q.
Equation of Kullback — Leibler
The “||” operator indicates “divergence” or Ps divergence from Q.
KL divergence can be calculated as the negative sum of the probability of each event in P multiplied by the log of the likelihood of the event in Q over the probability of the event in P.
Diversion for a given event
Implementation in Keras is as follow,
Regression Losses
In this, there are again various categories,
MSE ( Mean Square Error / Quadratic Loss / L2 Loss)
It is the average squared difference between observed and predicted values. e.g. in Linear Regression, we find lines that best describe the provided data points. Many lines can describe data points but which line to choose which best describes it can be found using MSE.
In below Diagram , predicted values are on the lines and actual values are shown by small circles.
Diagram showing Linear Regression
Error is the distance between data point and fitted line.
Mathematically, it can be stated as
Equation for Mean Square Error
Implementation in Keras is shown below,
Mean Absolute Error / L1 Loss
Now we move on to Absolute values no messing around, all round up now!
It computes the mean of squares of errors between labeled data and predicted data. It calculates the absolute difference between the current output and the expected output divided by the number of output.
Unlike Mean Squared Error , its not sensitive towards outliers.
Mathematically , it can be written as
Equation for Mean Square Error
As seen in the formula modules is added giving only the absolute values for the error.
Implementation in Keras is shown below,
Huber Loss / Smooth Mean
The best of both of worlds, iOS & Android both phones together? Nah..
Its Huber Loss function , it combines MSE and MAE ,if loss value is greater than delta use MAE, if less use MSE.
Mathematically , it can be written as
Equation of Huber LossComparision of Hubber (green ) , MAE (red) and MSE (blue)
Implementation in Keras is below
Now coming to Model Specific Losses they are mentioned with models in which they are used below,
MinMax (GAN Loss)
Generative Adversarial Networks uses this particular Loss and its a simple one ( that’s what they say).
Before this loss a little insight on GAN is needed so lets describe it in summary below
Two Neural Networks are involved.
One of the networks, the Generator, starts off with a random data distribution and tries to replicate a particular type of distribution.
The other network, the Discriminator, through subsequent training, gets better at classifying a forged distribution from a real one.
Both of these networks play a min-max game where one is trying to outsmart the other.
Now coming back to min-max loss, mathematically its equation is
Equation of Min — Max Loss
The generator tries to minimize this function while the discriminator tries to maximize it and it saturates for the generator, making it stop training it is behind discriminator …sad generator noises….
This can be further categorized into two parts 1) Discriminator Loss and 2) Generator Loss
Discriminator Loss
Discriminator train by classifies both the real data and the fake data from the generator.
It penalizes itself for misclassifying a real instance as fake, or a fake instance (created by the generator) as real, by maximizing the below mentioned function.
Equation for Discriminator Loss
log(D(x)) refers to the probability that the generator is rightly classifying the real image,
maximizing log(1-D(G(z))) would help it to correctly label the fake image that comes from the generator.
Generator Loss
While the generator is trained, it samples random noise and produces an output from that noise. The output then goes through the discriminator and gets classified as either “Real” or “Fake” based on the ability of the discriminator to tell one from the other.
The generator loss is then calculated from the discriminator’s classification — it gets rewarded if it successfully fools the discriminator, and gets penalized otherwise.
The following equation is minimized to training the generator,
Minimized to train the generator
This whole implementation of a Deep GAN Network along with loss in mentioned below as the loss is defined with respect to the model whole implementation needs to be referred.
It is extension of cross entropy loss function and it down-weight easy examples and focus training on hard negatives.
Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard misclassified examples. It is a dynamically scaled cross entropy loss, where the scaling factor decays to zero as confidence in the correct class increases.
Mathematics coming right up,
Focal Loss Equation
Focal Loss adds a factor (1−pt)^γ to the standard cross entropy criterion. Setting γ>0 reduces the relative loss for well-classified examples (pt>.5), putting more focus on hard, misclassified examples. Here there is tunable focusing parameter γ≥0.
Implementation in Keras is shown below,
Output of the loss function above
The whole notebook with each losses implemented is mentioned below,
The next Chatbot & Conversational AI Conference this November 15–17, 2022. This year’s event will be held on Zoom Events and in the Metaverse so that you can attend remotely from anywhere in the world!
The event will take you from Learning to Designing to Building a Conversation AI Agent during our 3-day event!
By the end of the event, you’ll have a Conversation AI Agent that you can share with your team or implement in your business, and you’ll get Certified!
Now let’s jump into the Agenda.
Zoom Events & the Metaverse
This year’s event will be held in ZOOM and Decentraland.
Why the Metaverse?
Conversational AI is already in the Metaverse. Inworld recently raised $50 Million for their platform, which creates AI-Powered Virtual Characters.
These characters can be used in games, in the metaverse, and online as brand ambassadors.
And yes, their co-founder, Kylan Gibbs, isalso our Keynote Speaker!
Our goal was to give you a sneak peek experience of what will be like when Conversational AI, Web3.0, NFTs, and the Metaverse begin merging.
So, we created such an experience!
AI Bots in the Metaverses: This BOT will live in the Metaverse in Decentraland and attend our Conference. It solves one of the significant challenges of hosting events online: the social aspect of introducing people to each other.
This bot will use your Personality NFT to make high-quality intros!
Our agenda is well thought out with you in mind. We start the day with the big picture of where the Conversational AI industry is now and where it is going.
The morning sessions feature Kylan Gibbs, co-founder & Chief Product Officer @ Inworld, who just raised $ 50 Million for creating AI-powered Digital Humans!
Morning Agenda: November 15, 2022
The Afternoon Sessions are where the rubber hits the road. We will do deep dives in areas of opportunity and look at execution.
The Afternoon has 3 Tracks.
Industry Track: Sessions in Finance, Banking, HR, Hospitality, Customer Centers, &
Execution Track: Design, Context, and NLU
Live Podcast: Interviews and Q&A
Afternoon Agenda: November 15, 20
Certified Workshop in Conversational UX & NLU
Day 2: November 16, 2022 | Certified Conversational UX Workshop
CDI will do a live workshop via Zoom. In this workshop, you will learn how to design a Conversational AI agent by learning the most useful design frameworks.
This is a hands-on workshop where you will apply your learning to your project.
By the end of the workshop, you’ll be able to earn a Certificate.
Day 3: November 17, 2022 | Certified AI Workshop
On Day 3 we will give your bot life….
Botcopy will share its latest framework for creating a create NLU system that leverages data and eliminates waste. This is a dramatic shift in how NLU systems are currently built and instead leverages a lean and intelligent building approach.
So what is the approach? And what will you learn?
First, you will learn all the Dialogflow basics for creating a Conversational AI Agent, such as intents, entities, context, fall back loops, etc.
Second, you will learn the latest building framework which is a combination of very smart onboarding, fallback loops, and data gathering which creates a system for creating the correct FAQs in the proper manner without guesswork!
This is a hands-on, full-day workshop where you will apply put together this framework!
By the end of the workshop, you’ll be able to earn a Certificate.
Join us Today
If you haven’t reserved your spot, hit the link below and join us in November!
Building the Metaverse With Open Source — Bot Libre Metaverse
The virtual experiences that are promised from the metaverse will make several ordinary experiences more captivating and more inclusive as more persons have access regardless of distance and even time. Students, teachers, co-workers, family, friends, romantic partners and even strangers getting to know each other can find deep connection, productivity and success from the metaverse.
It’s clear that people already understand the immense value of web3, in a recent Forbes Digital Assets article, “the market was worth $478.7 million in 2020, and experts expect it to grow to $800 billion in just four more years.”
With such open enthusiasm, an open-source platform is the necessary engine that will drive the growth of the metaverse. By using open source to create the metaverse, it is possible to advance initiatives supporting the creation of decentralized, distributed, and interoperable virtual worlds.
Fortunately, as shared by opensource.com, all of this is accomplished utilizing just already-available, industry-standard web technologies, as well as open-source software and content. So, while each creator will customize their virtual world, the development concepts remain the same.
Through Bot Libre, you already have access to and can download all the tools and material you need to start building the metaverse. The Bot Libre platform enables influencers, gamers, and businesses to engage the Metaverse by integrating true artificial intelligence and chatbots. Bot Libre bots can interact with users and navigate 3D spaces, Bot Libre provides an extensive API, integrations, and SDKs for popular 3D platforms.
Bot Libre is making significant strides. Through our Mozilla Hubs integration users can create their own private 3D virtual spaces. These spaces can be used for events like conferences, virtual storefronts and online classes. By adding a Bot Libre chatbot to your 3D space, you will provide ease of access to all attendees and prevent any customer from being overlooked.
Also, Bot Libre’s OMNI AI model combines vision deep learning models with NLP deep learning models to provide multiple senses, integrated learning, awareness and navigation in the Metaverse. This will allow businesses to be functional and fully engaged with users in the Metaverse .With these models, users can explore a variety of different locations to chat with Bot Libre’s chatbots and bots can walk around different 3D spaces following or guiding users through the space. With Bot Libre’s AI algorithms, the chatbot’s avatars can avoid obstacles and find the shortest path to locations and objects.
Bot Libre is now providing early access to unique metaverse solutions through our Beta Program.
We are looking for businesses, organizations, and developers that are interested in being early adopters of the Metaverse technologies, in order to shape, play, participate, and even profit from the Metaverse, by working with us to drive our open source metaverse AI solution. If you are interested in applying to be part of the program please contact sales@botlibre.biz .
Learned something? Please give us a
to say thanks and to help others find this article
Taking your business online is one of the most important tasks you can do, and there are E-commerce platforms out there like Shopify that make this task simple. Using Shopify, you can be up and running with your own E-commerce store in a matter of minutes.
Shopify lets you build beautiful, responsive, and professional-looking online stores without you actually having to know a lot about website development. And once you do have an online store, how do you ensure that you stay in touch with your customers 24 hours a day, 7 days a week? In one word: chatbots.
Adding a chatbot to your Shopify store can work wonders for your business, and, in this blog, we will teach you how you can go about adding a Dialogflow chatbot to your Shopify website.
Prerequisites:
Shopify account that has access to third-party app integration, the first plan-Basic plan has access.
Kommunicate account where you have already integrated the Dialogflow chatbot. If you have not integrated your chatbot yet, please refer to thisvideo. Also, you can check out this tutorial for creating a Dialogflow chatbot.
Chatbot Integration using Dialogflow CX
Before Dialogflow CX integration with Kommunicate, first make sure you have created a project and Dialogflow CX Agent from the Dialogflow CX console.
Then you have to enable the Dialogflow API for your project and create a service account key. Please refer to the links below.
Build your Dialogflow CX chatbot as per your requirements. Once the chatbot building is completed, tap on the Agent Settings option to create a service account.
Create a Service Account
In the following step, you will be giving the ROLE to the service account, select Owner/Editor as a ROLE.
Once ROLE assignment is done, proceed to create a JSON key for Integration.
Create a JSON Key
Click on Manage Keys and Add a JSON key.
Once you click on CREATE, a JSON file download will start. Upload the service account key file in Kommunicate dashboard along with the AGENT ID.
To get the Agent Id:
Go to Dialogflow CX console >> Select the Project >> In the Agent you have created ‘Copy ID’
It will be in the following format, projects/test-covid-rwvr/locations/global/agents/e2c5d8a3-f416–4f32-bfc9-d986d540abd here the Agent Id is e2c5d8a3-f416–4f32-bfc9-d986d540abdb
Copy the Agent Id.
Enter the Agent Id in Kommunicate Dashboard and then “Save and proceed.”
On successful integration, the bot will be given an ID(botId) and will be listed under the Manage Bots section. The botId will be used to identify the bot in the Kommunicate system.
You can use a similar method for Chatbot integration with Dialogflow ES too.
Now that you know how to integrate a Dialogflow Chatbot, it is time to add the Kommunicate chatbot onto your Shopify website.
Integrate Kommunicate Chatbot onto your Shopify website:
Step 1: Log into your Shopify account and click on the Apps tab. Search for the Kommunicate app in the search text bar.
Step 2: After searching, you will see the Kommunicate app listed, click on it >> Add app.
Step 3: Click on the Install App button available in the top right corner.
After clicking on the Install App, it will redirect you to the Kommunicate dashboard. Log into the Kommunicate account to finish the integration.
Step 4: Once you finish the integration, click on Go to Store and check the Kommunicate widget and Save the changes.
Now that the chatbot installation is complete, visit your store to check your chat widget.
Do not forget to select your Dialogflow bot in the RULES section of the Kommunicate dashboard, so that you can enable the the same chatbot on your Shopify store.
I was doing sentiment analysis for a data journalism article for a bootcamp’s final project. The tweets are Indonesian tweets about Kominfo’s recent blocking of Steam (and some other overseas platforms). You can read my article here (it’s only in Indonesian though). By the way, I’m using Python on Google Colab.
At first, I was using TextBlob as explained here, but the result was bad. I was sure that at least for #BlokirKominfo tweets, it must be mostly negative. Yet, it was mostly neutral, or positive, depending on where you draw the lines (because TextBlob outputs polarity in range of -1.0 to +1.0). Well, TextBlob only supports English, so the tweets were translated into English. Also, TextBlob is rule-based, while state-of-the-art NLP uses neural network. But it’s not like I can just train my own neural network model in a few days. I’m a total noob at NLP. Luckily, Huggingface has a lot of pretrained model available for free, and there exists an Indonesian one.
So let’s get started.
1. Registering for a Twitter developer account
Twitter now provides an easy and free developer account registration. Of course, it will have a lot of limits. For example, the API calls you can do within 15 minutes is limited. You also can only query for the past 7 days. If you need more, you can upgrade your essential (basic) account by paying or by applying for an elevated or academic account. If you just need more API calls, go for the elevated one, since it’s still fairly easy. It also gives you access to V1 API. By default, you have access to V2 API and it’s still incomplete, for example, you can’t get trending data. If you need to go further than 7 days, go for the academic one, but it’squite hard, since youneed to have a legit research as a researcher or graduate student.
Twitter developer platform home page
Okay, so how to get a developer account? Go to https://developer.twitter.com/ , click sign up at the upper right corner, then login with your Twitter account. You’ll then get a form. Fill the form, submit it, and you’re done. Make sure the save all of the tokens and secrets. But here we’ll only be using the bearer token.
2. Get tweets
First, you need to install Tweepy. You can do this using pip.
pip install tweepy
If you’re using google colab or jupyter, put a “!” at the front.
!pip install tweepy
Next, import it. “tw” here is just an alias to make it shorter.
import tweepy as tw
Now we create a client object using the bearer token. The wait_on_rate_limit argument being True tells Tweepy to wait for cooldown if you went past API rate limits.
Then we can finally query tweets. We’ll be using search_recent_tweets. If you have academic account or paid account that supports past 7 days, you can use search_all_tweets instead.
result = client.search_recent_tweets( query, start_time=start_time, end_time=end_time, max_results=max_results, next_token=next_token, tweet_fields=tweet_fields )
2.1. Arguments explanation
query would be your search query; it’s a required parameter. You can add “-is:retweet” at the end of your query to exclude retweets.
start_time and end_time defines the time window of your search. They’re in ISO 8601/RFC 3339 datetime format, which is YYYY-MM-DDTHH:mm:ssZ. It’s in UTC, so make sure you subtract your timezone difference to it. West Indonesia time is UTC+7, so today at 00:00 would be yesterday at 17:00 in UTC.
max_results is the max number of tweets in one API call. You can only have it 100 at most. If your query yields more than that, you’ll get a “next_token” attribute in the result’s metadata (not null). You can provide this next_token to get the “next page” of your query. If there’s no more tweets left to retrieve, next_token will be null. This should perfectly fit in a do-while loop, but python doesn’t have that, so let’s just set up a flag.
first = True next_token = None data = [] # This is just an example of combining the result while first or next_token: result = client.search_recent_tweets(...) data = result.data next_token = result.meta["next_token"] # This is just an example of combining the result data += data ...
tweet_fields tells the API which tweet attributes/fields to retrieve. In Tweepy, it’s a list of strings or a comma-separated string of field names. For example, if you want to retrieve the like count etc, you’ll want the public_metrics field. In my project, I used these fields:
Next, we’ll be using BERT to do sentiment analysis. We’ll use this model by mdhugol. It’s a sentiment classification model based on IndoBERT. It classifies text into positive, neutral, and negative ones (in this order of label). The website provides a field for you to try the model easily.
Field to try the model in Huggingface; LABEL_2 means negative
3.1. Init
Okay, for the model we’ll need to install transformer and emoji. Transformer package will be for the model, while emoji will be used to convert emojis into text that represents it. Just like before, add “!” at the front if you’re using google colab or jupyter.
pip install transformers emoji --upgrade
Next, we import them and define some constants. In addition to those packages, we’ll also use regex and html for cleaning.
from transformers import AutoTokenizer, AutoModelForSequenceClassification from transformers import pipeline import emoji import re import html
Before we do the sentiment analysis, we first need to clean the text. It’s not much, just removing or replacing some stuff. This function is based on preprocessing of indoBERTweet by indolem. It replaces user tags with @USER and urls with HTTPURL.
In addition to that, I’ll be replacing emojis with their text representation using emoji.demojize so the model will take emojis into account when determining the sentiment. However, it makes tweets longer, specially on those that spammed emojis. Meanwhile by default the model doesn’t support very long text. Twitter already has tweet length limit and it fits the model, but due to converting emojis, some tweets get very long. So I’m mitigating this by limiting repeating emojis to 3 repetition at most. I use regex to do this.
Lastly, I unescape HTML-escaped characters using html.unescape. When using TextBlob, I found that some tweets uses escaped HTML characters. It made TextBlob throw an error. Not sure what it does to BERT, but I’ll just unescape them.
Anyway, here’s the full preprocessing functions. Just use the preprocess_tweet function.
def find_url(string): # with valid conditions for urls in string regex = r"(?i)b((?:https?://|wwwd{0,3}[.]|[a-z0-9.-]+[.][a-z]{2,4}/)(?:[^s()<>]+|(([^s()<>]+|(([^s()<>]+)))*))+(?:(([^s()<>]+|(([^s()<>]+)))*)|[^s`!()[]{};:'".,<>?«»“”‘’]))" url = re.findall(regex,string) return [x[0] for x in url]
def preprocess_tweet(tweet): tweet = emoji.demojize(tweet).lower() tweet = re.sub(r"#w+", "#HASHTAG", tweet) tweet = re.sub(r"(:[^:]+:)1{2,}", r"111", tweet) tweet = re.sub(r"(:[^:]+:)(:[^:]+:)(1+2+){2,}", r"121212", tweet) new_tweet = [] for word in tweet.split(): if word[0] == '@' or word == '[username]': new_tweet.append('@USER') elif find_url(word) != []: new_tweet.append('HTTPURL') elif word == 'httpurl' or word == '[url]': new_tweet.append('HTTPURL') else: new_tweet.append(word) tweet = ' '.join(new_tweet) tweet = html.unescape(tweet) return tweet
3.3. BERT model
Okay, now that the text are good, we can do the sentiment analysis. We create a tokenizer and a model using the name of the pretrained model (pretrained_id). It will download from Huggingface so you need internet connection. It’s quite big, but Google Colab runs on Google’s servers and uses Google’s fast connection. Next, we create sentiment-analysis pipeline using the model and tokenizer.
Then, we just use the pipeline and convert the result label.
text = preprocess_tweet(text) result = sentiment_analysis(text) status = label_id[result[0]['label']] score = result[0]['score'] print(f'Text: {text} | Label : {status} ({score * 100:.3f}%)')
That’s it. Really. Sentiment analysis done. Huggingface pretrained models made it very easy to do. Well, if you can find the right model that fits your use case, that is. Otherwise you’ll have to adjust it or even retrain the model.
4. Using the Proper Model for the Tweet Language
Despite the query being an Indonesian event, not all tweets are in Indonesian. We can use the “lang” field of the tweet to know what language it is in (according to Twitter). With that, we can do sentiment analysis of a tweet using the proper model for the tweet’s language. In my project, I used BERTsent by rabindralamsal for English tweets. However, do note that different models may use different labels. BERTsent, for example, also classifies tweets into positive, neutral, and negative, but in the opposite order. In the Indonesian model, LABEL_2 means negative, but in BERTsent, it means positive. Here’s the constants by the way:
Well, that’s about it. It took a while figuring out how to do it, but once you know how, it’s actually rather easy. But of course, I didn’t do much text preprocessing. I didn’t make the model myself either. Actually, I don’t even have NLP basics, so please correct me if I wrote something wrong. Or, maybe, if you want to add something, please just do so in the comments. Thanks for reading.
WhatsApp For Business API 2022 — Complete Guide (Chapiter II)
Everything you need to know about WhatsApp chatbots in one place.
WhatsApp For Business Guide — Chapiter 2
1 — Setup your WhatsApp For Business account
In this tutorial, we will use Twilio as our main provider for this use-case. The biggest advantage using Twilio is the ability to perform tests using their shared WhatsApp For Business phone number. Once we create our account we will be able to join a channel and start messaging the chatbot.
2. Go the console page and click on Create New Account
Step 2 : Create new account
3. Let’s give a name to our account, I used : “Medium Bot Test”
Step 3 : name your project
4. Verify your email or phone number in order to continue the process
Step 4 : verify your identity
5. Answer these questions and make sure you use the same input
Step 5 : fill out the questions
Tadaaa!! we have successfully created a free account with $15.50 credits.
6. Activate the sandbox by clicking on confirm
Successful account creation
7. Send your first message to the bot
Twilio Sandbox for WhatsApp — send your first message
To begin testing, we will send a WhatsApp message from our device to +1 415 523 8886 with code join bow-gift.
Joining private channel in WhatsApp bot
8. Channel connection confirmation
Message received on Sandbox successfully.
2 — Generate your back-end application
The main component of this project is the back-end, it’s where all the processing occures. For this particular exercice, we will use JHipster to generate our back-end application in Spring Boot. It will speed up the development for a first MVP.
JHipster requirements :
Node.js (LTS)
Java 11 (JDK)
Make sure you have Node.js and Java 11 already downloaded and installed properly and then go to this tutorial to install JHipster : https://www.jhipster.tech/installation/
We will consider in this tutorial that you have already installed all requirements alongside with JHipster.
Initiate project in JHipster via CMD
Once you launch Node.js command line, create a folder called MediumBotBundle or whatever name that suits you, after that, run this command to start JHipster :
cmd -> jhipster
After that, the generator will ask you about the type of application, select Monolithic application.
Important : make sure you select the same options as shown in the image
After few minutes, the application will be generated and you will be able to start development. I personally use Intellij Community Edition as my main IDE and I highly recommand it for your future projects.
JHipster project structure
Create a class named “WhatsAppBusinessController” in web.rest package. The purpose of this class is to intercept all requests sent by Twilio to our WebHook when a user communicates with the bot.
Below is the JSON received in our interceptor :
JSON structure of Twilio message
We have 3 main attributes from this JSON message :
Users shouldn’t be guessing which option is available or not when they are searching for something.
Take this search as an example. National Car Rental website shows the user all the locations available to book a car for the dates already selected, but when you try to select one of them, two errors appears: either the store doesn’t open at the select date and time or the store is not valid for the promotion code added.
As you can see, there are many stories in the São Paulo (City in Brazil) area that supposedly is “available” to book (the red error only shows after your try to book), but after 2 minutes of clicking on all of them, only 2 or 3 were actually accepting bookings at given filters.
The only exception I’d like to mention is when the unavailable option is placed in the results as a marketing strategy.
As an excellent example, I can show Booking.com — which by the way does an awesome job with their UX.
They do show unavailable options (but notice here that the message about it is already visible to the user without having to click, it doesn’t leave the user confused thinking that’s available), but to induce a sense of FOMO, i.e., fear of missing out. They are indirectly saying “hey, look, this hotel is sold out because you were late, act now or you’ll miss more opportunities”.
Never use fonts too light in small texts, and always check with web accessibility evaluation tools if your design is compliant to WCAG rules to avoid law suits
3) FORGET TO DISABLE IT AND LACK OF FEEDBACK AFTER CLICKING ON A BUTTON.
Sometimes we need to load content after the whole site is already there. But if that new content pushes everything down when being placed, you got a UX problem here.
Since the interface is already loaded, the user might be in the middle of an action, a click, drag, and drop. And having the content pushed down might cause unexpected behavior if the user clicks somewhere at the same moment that the new content appears.
The example below shows a shopping cart after adding a product. The e-commerce then loads related products on top of the cart after the content has already been loaded. If the user tries to change the quantity of the first product in the cart right at the moment that the related products are being loaded, it ends up clicking on one of them and going to the product page, frustrating the experience of buying that first product (Yeah, I did that many times, I always forget to wait for the related products, but this shouldn’t be an issue the user needs to deal with).
The Role of AI in the Transformation of the Insurance Industry — DATAVERSITY
The insurance industry has been traditionally conservative with technology advances and hesitant to adopt new technologies. However, times are changing, and artificial intelligence (AI) is gaining much attention from insurance companies, who are starting to realize the important role that AI can play in their operations.
AI in the insurance industry is poised to bring another wave of disruption and innovation to this $5.3 trillion global market.
GET UNLIMITED ACCESS TO 140+ ONLINE COURSES
Choose from a wide range of on-demand Data Management courses and comprehensive training programs with our professional subscription.
According to McKinsey & Company, there’ll be 1 trillion connected devices by 2025. This will help collect tons of data to enhance the power of AI in the insurance industry even further.
Competitors are taking every possible step to claim their digital market share by investing huge amounts in digital transformation. And one of the key points in this process is how to reduce costs and expenses.
Top 5 Use Cases of AI in the Insurance Industry
Let’s look at the capabilities of AI in the insurance industry.
1. Faster Claims Processing With NLP
Natural language processing (NLP) refers to algorithms that can understand human speech or text from written documents and convert them into plain language or other formats.
It’s essentially a form of machine learning that enables computers to understand human language without having to write programming instructions for each word or phrase.
The claims management process can be very costly, both in terms of time and money. Up to 50%-80% of premiums ‘ revenues can be eaten up by this process, which is largely paper-based and rarely digitized. That’s where NLP can save resources for insurance companies.
Many industries already use NLP:
However, the use cases of NLP and AI in the insurance industry are still evolving.
For example, as part of its Intelligent Production platform, Swiss Re is using NLP to automate some parts of:
Claims management processes
Customer communication
Underwriting
2. Rapid Document Digitization with OCR
The first step to any data analytics project is collecting and organizing your data, which can be time-consuming and tedious for large organizations.
One way to automate this process is to use optical character recognition (OCR) software, which converts scanned images into text that can be easily searched by keyword or indexing software.
Image recognition is one area of AI in the insurance industry that can save a lot of money. In fact, it can drive up to 80% in cost savings for individual processes.
Some of the use cases for OCR include:
Here’s an example of how OCR works:
An insurer could use OCR to scan documents from claims files from multiple sources. The sources can be medical records from doctors’ offices, police reports from law enforcement agencies, or claims forms filed by clients.
Then, they input the text into a central database where it can be analyzed by machine learning algorithms and compared against other similar documents.
3. Insurance Fraud Detection and Prevention
Insurance fraud is a major industry problem, but AI can help companies detect fraudulent claims before they become a significant issue.
A recent study by the Federal Bureau of Investigation revealed that insurance fraud (non-health insurance) costs U.S. insurance companies close to $40 billion annually.
AI can analyze patterns in past data and determine whether something looks suspicious. AI-powered systems can analyze thousands of data points per second and detect anomalies more accurately than humans ever could alone.
Here’s how AI can help in insurance fraud detection and prevention:
4. Accelerated Claims Adjudication with Visual Image Recognition
AI can analyze images, which makes it a valuable tool for insurance companies. Insurance claims adjusters are often asked to evaluate photos of damaged property or vehicles to determine what needs to be repaired or replaced.
Insurance companies can use machine learning to exploit behavioral data, such as facial expressions or the tone of voice, at the moment of underwriting.
This is especially common in life insurance or health insurance, where it’s been estimated that over 40% of risk information can be gathered from behavior monitoring alone.
However, human error often leads to inaccurate estimates. AI-based claims management systems can quickly and effectively process:
This allows organizations to make more informed decisions about their claims processes and ultimately improve customer satisfaction.
5. Improving Customer Experience
By leveraging conversational AI, insurers can automate repetitive tasks such as answering questions about policy features or claims to improve their customer experience.
AI-driven chatbots can:
Insurance companies can also use technology such as predictive text analytics that uses machine learning algorithms. They can analyze past customer conversations or unstructured data like emails or social media posts.
Conclusion
Advancements in machine learning, deep learning, and AI are making their way into the insurance industry and changing how enterprise insurance software is built. This, in turn, will help to reduce defensibility among traditional enterprise insurers and increase competition.
There’s great potential to innovate with AI in the field of insurance. That’s because the proliferation of data makes it easier to predict fraud, mitigate risk exposure, provide more personalized policies, and settle claims more quickly.
