Comparative Study of Sentiment Analysis for Multi-Sourced Social Media Platforms
This is an incremental study comparing existing methods for sentiment analysis on social media data, relevant for researchers and practitioners in NLP.
The paper tackled sentiment analysis on multi-sourced social media data by comparing lexicon-based, machine learning, and deep learning approaches, with results showing that LSTM achieved the highest accuracy at 85%.
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.