Comparative Study of Sentiment Analysis for Multi-Sourced Social Media Platforms

TL;DR

This paper compares different sentiment analysis techniques—lexicon-based, machine learning, and deep learning—applied to multi-source social media data from platforms like Twitter and Reddit.

Contribution

It provides a comparative analysis of three sentiment analysis approaches on multi-source social media datasets, highlighting their effectiveness and differences.

Findings

Deep learning (LSTM) outperforms other methods in accuracy.

Lexicon-based approach is faster but less accurate.

Machine learning (Naive Bayes) offers a balance between speed and accuracy.

Abstract

There is a vast amount of data generated every second due to the rapidly growing technology in the current world. This area of research attempts to determine the feelings or opinions of people on social media posts. The dataset we used was a multi-source dataset from the comment section of various social networking sites like Twitter, Reddit, etc. Natural Language Processing Techniques were employed to perform sentiment analysis on the obtained dataset. In this paper, we provide a comparative analysis using techniques of lexicon-based, machine learning and deep learning approaches. The Machine Learning algorithm used in this work is Naive Bayes, the Lexicon-based approach used in this work is TextBlob, and the deep-learning algorithm used in this work is LSTM.