SENTIMENT ANALYSIS OF TWITTER DATA USING KALMAN FILTERS AND LSTM FOR POLITICAL OPINION PREDICTION
DOI:
https://doi.org/10.29121/ijetmr.v10.i6.2023.1604Keywords:
Sentiment, Twitter Data, Kalman, Political, Prediction, LstmAbstract
In the contemporary digital age, social networking platforms like Twitter play a significant role in shaping public opinion and disseminating information. Twitter, as a micro-blogging site, generates vast amounts of real-time data that can be utilized for various applications, such as sentiment analysis, market prediction, and political insights. Sentiment analysis involves extracting subjective information from large datasets and classifying the data into various sentiment categories, such as positive, negative, or neutral.
This research aims to enhance sentiment classification by integrating Kalman filters and Long Short-Term Memory (LSTM) networks. Kalman filters are used for smoothing noisy data and providing more accurate predictions, while LSTM, a type of recurrent neural network, is employed to capture long-term dependencies in sequential data. The study processes Twitter data related to Indian political parties, using both Kalman filters and LSTM for sentiment analysis. The goal is to predict public sentiment towards different political parties, thereby offering insights into the political landscape.
By applying these advanced techniques, the research compares the effectiveness of Kalman filtering and LSTM networks for classifying sentiment, and evaluates which approach provides superior accuracy in predicting sentiments expressed in tweets. The findings contribute to the understanding of public opinion dynamics and the performance of political parties based on sentiment analysis from Twitter data.
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Copyright (c) 2023 Divyanshu Negi, Abhinandan, Jasveer, Shika Taneja
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