Sentiment Analysis using Deep Learning techniques with India Elections 2019

Sentiment Analysis using Deep Learning techniques with India Elections 2019

Predict BJP Congress Sentiment using Deep Learning. The phenomenal growth in real-time data tracking and analyzing techniques has inspired data scientists to visualize and predict sentiments, build real-time models to predict the winners, etc.

Predict BJP Congress Sentiment using Deep Learning

The phenomenal growth in real-time data tracking and analyzing techniques has inspired data scientists to visualize and predict sentiments, build real-time models to predict the winners, etc.

Trust me, the most exciting part of it is capturing the information online from all sources and predict in real-time with the highest accuracy. The great challenge in this scenario is the accuracy and ever-increasing length of the date getting flooded from all sources every second. With the current challenges in view, I decided to use a few Deep Learning ML techniques to predict moods using Twitter data.

Source

Note that this article assumes a basic knowledge of data science and NLP (Natural Language Processing). But if you are a newcomer to this world, I have provided links throughout the article to help you out. This blog is structured like this:

Describe deep learning algorithms, LSTM, Bi-directional LSTM, Bi-directional GRU, CNN. Train these algorithms using contextual election corpus as well as pre-trained word embeddings to predict sentiments of electing parties. Comparing the accuracy and log loss of different models.

Glove Pre-trained Word Embeddings

Source, License — Apache Verison 2.0

We started our sentiment classification technique with Google’s pre-trained Word2Vec model that represents words as vectors, built on the basis of aggregated global word-word co-occurrence statistics from a corpus. The Word2Vec model, trained by Google predicts words close to the target word with a neural network to represent linear substructures of the word vector space.

As we represent each word with a vector and a sentence (tweet) as an average of its words (vectors) to illustrate its sentiment, it becomes obvious to train the word vector with different moods to aid in the classification and prediction process. As such, Word2Vec is trained with different RNN models.

Recurrent Neural Networks

recurrent neural network (RNN) is a sequence of inter-linked artificial neural networks where connections between nodes form a directed graph along a sequence. They are particularly known for processing data related to sequence: text, time series, videos, etc where the output at any given instant t is affected by the output at previous instant t-1 along with the input at t.

Source: License-Obtained

We will see how RNN based models (LSTM, GRU, Bi-directional LSTM) perform with an external embedding which has been trained and distilled on a very large corpus of data as well as with an internal embedding, where a part of the contextual corpus has been considered for training.

Basic RNNs suffers from vanishing and exploding gradient problems for which LSTM based networks have evolved to handle this problem.

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