Sentiment analysis on (Bengali horoscope) corpus

Ghosal, Tirthankar; Das, Sajal; Bhattacharjee, Saprativa

doi:10.1109/indicon.2015.7443551

Cited by 22 publications

(15 citation statements)

References 5 publications

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“…This system achieved 0.72 f-score using a multinomial Naive Bayes classifier with bigram features. Another model of sentiment analysis on the Bengali horoscope corpus is proposed based on SVM, which achieved 98.7% accuracy using unigram features [6]. It observed that most of the research activities have been conducted so far, considered a small dataset of Bengali sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial Naïve Bayes

Hossain

Sharif

Hoque

2021

Advances in Intelligent Systems and Computing

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Recently, sentiment polarity detection has increased attention to NLP researchers due to the massive availability of customer's opinions or reviews in the online platform. Due to the continued expansion of e-commerce sites, the rate of purchase of various products, including books, are growing enormously among the people. Reader's opinions/reviews affect the buying decision of a customer in most cases. This work introduces a machine learning-based technique to determine sentiment polarities (either positive or negative category) from Bengali book reviews. To assess the effectiveness of the proposed technique, a corpus with 2000 reviews on Bengali books is developed. A comparative analysis with various approaches (such as logistic regression, naive Bayes, SVM, and SGD) also performed by taking into consideration of the unigram, bigram, and trigram features, respectively. Experimental result reveals that the multinomial Naive Bayes with unigram feature outperforms the other techniques with 84% accuracy on the test set.

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Section: Related Workmentioning

confidence: 99%

Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial Naïve Bayes

Hossain

Sharif

Hoque

2021

Advances in Intelligent Systems and Computing

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“…In 2015, Ghosal et al [68] performed sentiment analysis on Bangla horoscope data collected from daily newspapers using various classification models and evaluated the best model. The sentiment polarity had two classes (positive and negative).…”

Section: ) Machine Learning Approachesmentioning

confidence: 99%

“…Ghosal et al [68] Bangla horoscope data from the daily newspaper over a year was used. The highest result obtained from SVM: accuracy of 98.7%.…”

Section: Mandal Et Al [22]mentioning

confidence: 99%

Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods

Sen,

Fuad,

Islam

et al. 2021

Preprint

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The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and technical knowledge, journals, and documentation. Consequently, many Bangla-speaking people, who have limited command of English, face hurdles to utilize English resources. To bridge the gap between limited support and increasing demand, researchers conducted many experiments and developed valuable tools and techniques to create and process Bangla language materials. Many efforts are also ongoing to make it easy to use the Bangla language in the online and technical domains. There are some review papers to understand the past, previous, and future Bangla Natural Language Processing (BNLP) trends. The studies are mainly concentrated on the specific domains of BNLP, such as sentiment analysis, speech recognition, optical character recognition, and text summarization. There is an apparent scarcity of resources that contain a comprehensive study of the recent BNLP tools and methods. Therefore, in this paper, we present a thorough review of 71 BNLP research papers and categorize them into 11 categories,

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“…Tirthankar Ghosal and Sajal K. Das mainly focus on the sentiment analysis of Bengali daily horoscope using SVM with unigram features on the paper. They are given the positive and negative emotional basis of the sentence by crawling a leading Bengali newspaper's daily horoscope section [6].…”

Section: Related Previous Workmentioning

confidence: 99%

“…MaxEnt and SVM algorithms are compared in paper [7] for sentiment analysis on Bengali microblog posts with different feature extraction methods and it gets the best performance with SVM with unigram and emoticons as features. Also, sentiment analysis is done on the Bengali horoscope corpus in paper [8] using ML algorithms. NB, SVM, K-Nearest Neighbors (NN), Decision Trees (DT), and Random Forest (RF).…”

Section: Related Previous Workmentioning

confidence: 99%

Identifying and Categorizing Opinions Expressed in Bangla Sentences using Deep Learning Technique

Rahman¹,

Haque²,

Saurav³

2020

IJCA

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Identifying and categorizing opinions in a sentence is the most prominent branch of natural language processing. It deals with the text classification to determine the intention of the author of the text. The intention can be for the presentation of happiness, sadness, patriotism, disgust, advice, etc. Most of the research work on opinion or sentiment analysis is in the English language. Bengali corpus is increasing day by day. A large number of online News portals publish their articles in Bengali language and a few News portals have the comment section that allows expressing the opinion of people. Here a research work has been done on Bengali Sports news comments published in different newspapers to train a deep learning model that will be able to categorize a comment according to its sentiment. Comments are collected and separated based on immanent sentiment.

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Sentiment analysis on (Bengali horoscope) corpus

Cited by 22 publications

References 5 publications

Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial Naïve Bayes

Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial Naïve Bayes

Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods

Identifying and Categorizing Opinions Expressed in Bangla Sentences using Deep Learning Technique

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