City-Sentiment-Detector-BERT

Classifying tweets from cities into Positive and Negative sentiment and determining happiness of that city. Made this project to gauge the sentiment of different cities across the country during the Farm Law Protests during Dec 2020 - Jan 2021.

Project Description:

The aim of this project is to perform sentiment analysis of tweets collected from selected cities and get an overall sentiment of that city. Project consists of:-

• Pretrained BERT base model fine tuned on 'Sentiment140 Dataset' by Stanford University which is 1.6 million row dataset consisting of tweets. The fine tuned model gave an accuracy of 86+ % compared to algorithms like Logistic Regression, Naive Bayes, etc which gave an accuracy of 77% only.

The model was retrained on single epoch consisting on ~65k steps over a period of 7 days.

Running project locally:

• Install all libraries :- transformers, google_trans_new, tweepy, pandas, numpy, tensorflow, nltk, bs4 and re
• Create twitter developer account and get api credentials and input them in 'TweetMining'.
• Run 'TweetMining' Notebook to get live tweets.
• Run 'BERT_Sentiment140' notebook to classify tweets and to get city happiness score.

Data Preprocessing:

• Extracted only 'text' & 'target' columns
• Dropped null values
• Cleaned tweets by removing links, xml, converting text to lowecase and tokenising words
• Shuffled rows of dataframe to get balanced dataset (Can be improved more)
• Transformed label '4' to '1' in dataset so as to use SparseCategoricalCrossentropy loss
• Converted dataset to BERT example format
• Converted above format to tf format

Training:

• Preprocessed data is passed to BERT model for finetuning. 'Adam' optimizer (learning_rate=3e-5,epsilon=1e-08,clipnorm = 1.0) is used along with 'SparseCategoricalCrossentropy' loss and 'SparseCategoricalAccuracy' accuracy.
• Model is retrained on 1 epoch with ~65k steps. Accuracy of over 86% was achieved after training for 7 days.

Project Flow:

• Connection to twitter api is established using tweepy wrapper for python using credentials.
• 1000 Tweets are mined according from different cities by entering city lat, lon and radius.
• The tweets are cleaned and non english tweets are translated by using google translate api.
• The cleaned and translated tweets are stored as dataframe with columns 'text' and 'city'
• The dataframe is passed through the retrained model with a softmax layer added for classification.
• The postive and negative sentiments of each tweet per city are counted and a ratio is postive tweets to negative is taken to guage the sentiment of that particular city.

Modules:

• 'BERT_Sentiment140' is the main notebook where finetuning and sentiment analysis is performed.
• 'TweetbyLoc' is a util module for establishing connection to twitter api and mining tweets by location using tweepy wrapper.
• 'TweetMining' is the notebook where 'TweetbyLoc' is called and where tweets are mined, translated and stored in a csv format.

Further Improvements to be made:

• Making code more modular.
• User input for cities instead of predefined cities.
• Creating and deploying a dashboard for displaying tweets mined with a choropleth map visualising sentiments of the cities.

Note: All processed csv files are created and stored in 'data' directory.