Note - Please put your own API Keys and Servers have been removed from cloud.
SHADE Engine to detect the emotions of a person based on his/her social media activity and recommend measures to improve upon the same.
- Data is fetched not only from Twitter but also from Medium.com and Instagram
- Emotion detection is applied on photos shared on Instagram apart from text
- Derive personality insights apart from emotion
- Graphical representation of data using Google Charts (Bar Graph, Donut Pie-chart, 3D chart etc)
- Recommendation engine for providing tips based upon emotion detected
- Use of Language Translator module to translate all tweets to English so that other IBM Watson services can use it effectively
- Based on emotion detected, various yoga positions have been displayed
- If user is sad or feeling disgusted, it is very dangerous to leave him alone. So we have shown nearby temples, hotels and natural places using Google Nearby APIs so that he will come out of room and explore places and will not be alone in public places.
- To show the various places like hotels, temples and natural places, application uses the current location of the user.
Help me with my Mood with Social-media Health Analysis and Display Engine (SHADE) is a software solution which tries to analyse your current emotions based on the content that you share on different social media websites. With advances in technology, it has now become easy to detect the emotions user is going though by using NLP on the text shared which when combined with visual recognition on the images gives a concrete solution to take calm down measures before hand he/she chooses any drastic actions.
- The social media websites that we are targeting to understand user's current emotions are :
- Twitter : We see twitter as a place where people express their instant emotions about Named Entities(Name,place,Product,Organisation etc).
- Instagram : We see instagram as a rich source of data because of the #Tags usage and images a user shares which points to his current mental well being
- Medium : The earlier two websites are used by people to express their momentary emotions where as when it comes to the most popular blog website like medium, analysing user's blogs gives you deep insights about his interests,personality and his state of mind.
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The software solution will have the following components :
- Data Aggregator : It will be a REST Server to aggregate data from different social media websites and put it in a NoSQL DB
- Data Analyser : It shall use the data aagregated previously and use IBM Watson components like Language Transaltor,NLP and Analytics,Tone Analyser,Personality Insights,Custom Models to understand user's state of mind.
- Suggested Measures : Once the user's state of mind has been understood. We shall give him data visualizayions to help him understand himself better and songs,videos,articles,yoga asanas and nearby places to eat,worship or of natural beauty depending the most prominent emotion detected.
- ibm-aggregator :
- Server-Type : REST
- Programming Language : Python
- App : Flask App
- Hosted : heroku
- Major API Endpoint : http://ibm-aggregator.herokuapp.com/aggregate/valid_mediumID/valid_twitterID/valid_instagramID
- 3rd Party APIs used : Twitter REST API,Medium RSS Feed, Instagram APIs.
- ibmanalyser :
- Server-Type : NodeJS Project (Web-APP + REST Server)
- Programming Language : NodeJS with Express Framework
- App : NodeJS App
- Hosted : IBM Cloud
- Database : MongoDB hosted on MLAB
- Major API Endpoint : https://ibmanalyser.eu-gb.mybluemix.net/readProfile/valid_twitterID/valid_mediumID/valid_instagramID
- 3rd Party APIs used : Youtube APIs, Nearby Places APIs,Google Chart APIs,Algorithmia Models,IBM Custom Model APIs,ibm-recommender APIs, IBM - Language Transaltor,NLP and Analytics,Tone Analyser,Personality insights etc
- ibm-recommender :
- Server-Type : REST
- Programming Language : Python
- App : Flask App
- Hosted : heroku
- Major API Endpoint : http://ibm-recommender.herokuapp.com/recommendations/emotion_detected
- Machine Learning Models Used : LDA and Tf-IDF
- Data for ML Models : CSVs with tips from doctors and psychologists to fight different emotions.
- For instagram, business accounts were having a 15days approval period to get access to APIs but since we dint have that much of time, we used a hack wherein the instagram data of any user can be obtained from this URL : https://www.instagram.com/iam_niks026/?__a=1 where iam_niks026 is the "TwitterHandle".So currently we have used that as the JSON Response and kept it on server and built a JSON parser on top of it. When the solution goes live the FILE Reading will be replaced by the API calling.
- Python Libraries Used : Flask,tweepy,json,csv,requests,xml.etree.ElementTree
- For each user we have a single document in the collection "aggregate" fo DB "ibm" in MongoDB. When the user first enters the system, ibm-aggregator checks the DB if these set of social-media IDs exist in DB. If not implies he has come to the web app for the first time and a new document is created for him.If not then the previous document is deleted and a new one is created for him.
- Motivation : To get user's tweets of past 7 days to undertsand what were the instantaneoud emotions he went though.
- Data collected : For each tweet, we collect tweet text,time and language of tweet.
- Motivation : To get hash tags of each post which convey user's current state of mind and perform visual recognition on images.
- Data collected : For each post, we collect post's hashtages and post's image URL and number of likes and store it in DB
- Motivation : To get insights into user's perosnality and interests.
- Data Collected : For each blog, the blog content and date of publish.
- When user provides the social media handles the User Interface first calls the ibm-aggregator module to fetch the data into DB which would be then analysed.
- Typical API Call looks like : http://ibm-aggregator.herokuapp.com/aggregate/oldirony/amitabhtiwari3/amita_pandey
- Now the DB Document created for this user looks like:
- NodeJS Modules Used : watson-developer-cloud,image-downloader,request,async,body-parser,request,fs,algorithmia etc
- When ibm-aggregator has successfully agregated the data, the UI calls ibm-analyser to enrich the data with emotion intelligence.
- Motivation : Using the tweet language field , convert tweets in other language to english.
- Outcome : Now all the tweets have been converted to english.
- Motivation : To analyse the tone behind the set of words tweeted by the user.
- Outcome : To get scores of different emotions like sad,happy etc and add them to each tweet object and the one with highest score is chosen as the prominent emotion .
- Motivation : It is applied to Tweets to get keywords and Entities the user is influenced by.
- Outcome : Keywords array and Entities array added to user's document.
- Motivation : It is applied to Blogs and Instagram Hashtags to know the personality attributes of user.
- Outcome :Personality insights array added to user's document with different parent quality and corresponding children quality.
- Motivation : to create a customised 2-layer Network of Models to understand the emotions involved in an image for each instagram post.
- First Layer Model : Algorithmia model - To find if the image has any human faces involved in it. If YES then the face emotions are extracted out to be associated with image.
- Second Layer Model : Watson Custom Model - when first layer model confirms that there is no human being in the image then we make use of aesthetics of the image to determine emotion with a model trained with two sets of images like ones that are charcaterized by colors like black,grey,dark shades of blue and the other with much more vibrant colors. The first signifie sthat the user is feeling low while the second is an indicator of joy.
- Outcome : Each instagram post object is updated with the associated emotion.
- When ibm-aggregator confirms that the data has been aggregated in DB, ibm-analyser comesinto action and does perform all the analysis and updates the user docuemnt with enriched analytical results.
- Typical API Call looks like : ibmanalyser.eu-gb.mybluemix.net/users/readProfile/amitabhtiwari3/oldirony/pandey_amita
- Now the DB Document created for this user looks like:
- Python packages Used : numpy,pandas,gensim,nltksklearn,pyLDAvis,datetime,kmodes,pickle etc
- After ibm-analyser has updated the user document,based on emotion the UI calls the recommender for pro-tips from doctors and psychologists to fight the adverse effects of emotions. This REST Server acting as recommendation engine then returns a set of 5 articles obtained from LDA and TF-IDF Model.
- Motivation : Using API input, where the emotion param is a piece of text describing the user's state, the LDA Model using topic modelling on the corpus of articles it has, tries to find related articles.
- Outcome : 2 Articles from LDA are concatenated to JSON Response.
- Motivation : It tries to cluster articles based on term weights and uses cosine similarity between the input param and each vector representation of article from corpus and returns top 3 srticles with maximum matching scores.
- Outcome : 3 Articles from LDA are concatenated to JSON Response.
- Both Models have been pickled and at run time the pickled representation of them are used to come up with a set of 5 articles from the corpus that are most similar to the input param text received at th ePAI endpoint.
- Typical API Call looks like : http://ibm-recommender.herokuapp.com/recommendations/fear
- Snapshots : Recommendation Engine and REST-Response
- Motivation : To come up with a dashboard with data visualisations to help user understand himself and the real-word entities that are influencing his state of mind. Also to come up with video,songs and articles recommendations based on his prominent emotion. Plus to come up with Nearby places to explore and Yoga Asanas to practice to overcome the diffcult times.
- After ibm-analyser has updated the user document, the UI calls the Recommender for articles,Youtube APIs for songs and videos based on his current emotion. Google charts APIs are called for data visulaisations and Nearby APIs for places to eat,worship or explore natural beauty. Yoga Asanas Carousels are created at run time depending on his state of mind.
- Motivation : To get funny content and relaxing music.
- Outcome : 3 responses for videos and songs. User can click on it and he will be redirected to youtube site to play the same.
- Motivation : To get professional tips on how to deal with certain kind of emotions.
- Outcome : 5 tips are presented on the dashboard.
- Motivation : To cheer up people with depressions, psychologists says it's important to get out of your home, visit places that have good vibes to calm down your mind. The app asks the permission to know the current location of the user. Then the Nearby APIs are called to get list of restaurants,holy places and natural beauties around that location.
- Outcome : Lists of places, clicking on any list item, redirects to google maps with the the source as your current location and destination as the Latitude,Longitude of list item you clicked on giving you a glimpse of how to reach the destination./li>
- Motivation : There are different Yoga positions to practice depending on the emotion you are feeling.
- Outcome : Same is presented to user depending on whether he is feeling happy,sad or disgusting.
- Motivation : To come up with a dashboard to help user undertand himself better.
- Outcome : Different kinds of graphs and pie-charts depicted below.
- UI does API calls and gets the dashboard populated with data from DB and external 3rd party APIs.
- Few Snapshots : UI elements are as follows.
