Bangla Text Sentiment Analysis Using Supervised Machine Learning with Extended Lexicon Dictionary

Abstract

With the proliferation of the Internet's social digital content, sentiment analysis (SA) has gained a wide research interest in natural language processing (NLP). A few significant research has been done in Bangla language domain because of having intricate grammatical structure on text. This paper focuses on SA in the context of Bangla language. Firstly, a specific domain-based categorical weighted lexicon data dictionary (LDD) is developed for analyzing sentiments in Bangla. This LDD is developed by applying the concepts of normalization, tokenization, and stemming to two Bangla datasets available in GitHub repository. Secondly, a novel rule-based algorithm termed as Bangla Text Sentiment Score (BTSC) is developed for detecting sentence polarity. This algorithm considers parts of speech tagger words and special characters to generate a score of a word and thus that of a sentence and a blog. The BTSC algorithm along with the LDD is applied to extract sentiments by generating scores of the two Bangla datasets. Thirdly, two feature matrices are developed by applying term frequency-inverse document frequency (tf-idf) to the two datasets, and by using the corresponding BTSC scores. Next, supervised machine learning classifiers are applied to the feature matrices. Results show that for the case of BiGram feature, support vector machine (SVM) achieves the best classification accuracy of 82.21% indicating the effectiveness of BTSC algorithm in Bangla SA.

Exploring Sentiment Analysis in Indian Regional Languages: Methods, Challenges, and Future Directions

“…These studies have addressed challenges such as informal language, slang, and emoticon usage typical of social media text, with BERT models demonstrating superior performance due to their ability to capture contextual information effectively. The exploration of these models across various languages and contexts emphasizes the dynamic nature of sentiment analysis research and its potential for future advancements [ 1,3,4,5,7,9,10,11,13,14,15,20,22,23,24,30].For languages with limited computational resources, such as Marathi and Urdu, lexicon-based approaches have been proposed as effective methods for sentiment analysis. Researchers have developed lexicons that include lists of positive and negative words, assigning polarity values to facilitate the classification of sentences into sentiments.…”

“…Future work is suggested to focus on expanding the classification capabilities to include figurative language, enriching datasets with more diverse samples, exploring additional algorithms for enhanced accuracy, and further developing sentiment analysis models to accommodate low-resource languages. These directions underscore the evolving nature of sentiment analysis research and its critical role in understanding and leveraging user-generated content in multilingual societies [1,2,3,4,5,7,9,10,11,13,14,15,20,22,23,24,30,31,32].…”

Exploring Visitor Sentiments: A Study of Nusantara Temple Reviews on TripAdvisor Using Machine Learning

“…Stopword elimination is removing words that do not add to the significance of the text [22]. BoW is a method that quantifies the frequency of word appearances in a text and converts them into input vectors for analysis or categorization [23]. Every word in the text is tallied based on how often it appears in the document.…”

A Rule-Based Sentiment Analysis of WhatsApp Reviews in Telugu Language

“…Bhowmik and Arifuzzaman [24] apply sentiment analysis on Bangla language. This paper uses the concepts of text processing such as normalization and tokenization along with using stemming to process text.…”