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3 articles for “Lexicon-based”
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A Review on Punjabi Language Sentiment Analysis Using Machine Learning
Abstract: In this computer era, everyone is expressing their opinions or sentiments not only physically but over the social media platforms very frequently. Earlier, English was the only language for exchanging thoughts over the web, however, in the recent times, feedbacks, surveys or comments by the users are expressed in regional languages too. This leads to the need of analyzing sentiments for better communication on the digital platforms. For example, if …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 116–122 Read article
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AI-Based Sentiment Analysis of Public Perception Under Bangabandhu Sheikh Mujibur Rahman
Abstract: Sheikh Mujibur Rahman, known as Bangabandhu, played a pivotal role in Bangladesh’s post-liberation period (1971–1975). Understanding public sentiment during his leadership is crucial for historical analysis. This study employs Artificial Intelligence (AI)-based Sentiment Analysis to examine public perception through archived newspapers, parliamentary speeches, and historical records. Using Natural Language Processing (NLP) techniques, including sentiment classification and opinion mining, we analyze textual data to assess the prevailing public mood during his …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 2, 2025 · pp. 21–29 Read article
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Optimized Sentiment Analysis Through TextBlob and Hybrid RNN Models
Abstract: In today’s world, analyzing people’s feelings from what they write online has become very important. This is because there is a large amount of content created by users. To make this analysis accurate and fast, we present a method. This method uses a mix of two approaches: one that looks up words in a dictionary and another that uses computer learning. TextBlob is an affordable tool for getting an initial …
Published in International Journal of Computer Science Languages · Vol. 4, Issue 1, 2026 · pp. 29–28 Read article