Sentiment detection from Bangla text using contextual valency analysis

Hasan, K. M. Azharul; Rahman, M Asifur; Badiuzzaman,

doi:10.1109/iccitechn.2014.7073151

Cited by 42 publications

(10 citation statements)

References 8 publications

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“…Hasan K.A. et al [12] used SentiWordNet and WordNet to determine the polarity and meaning of words of the text to develop their methods for detecting sentiment from text written in the Bangla language. Chowdhury S. et al [13] used the SVM and MaxEnt (Maximum Entropy) algorithm to extract sentiments from Bangla Microblog (Twitter) posts automatically, regardless of whether the polarity of the text is positive or negative.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis on Bengali Facebook Comments To Predict Fan's Emotions Towards a Celebrity

et al. 2021

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In this present era, sentiment analysis is considered as one of the most rapidly growing fields of computer science study. It is a text mining technique which is automated and determines the emotion of a text. A text can be divided into many emotions using sentiment analysis. Since there are some studies on emotion analysis in the Bangla language, it is regarded as a key research area in the field of analyzing Bangla language. This paper works with five different emotions and those are Happy, Sad, Angry, Surprise and Excited. Apart from these emotions our paper also deals with two categories, such as Abusive and Religious. We proposed a method of machine learning technique which is the SVM algorithm to extract these five individual emotions from Bangla text.

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Section: Related Workmentioning

confidence: 99%

Sentiment Analysis on Bengali Facebook Comments To Predict Fan's Emotions Towards a Celebrity

et al. 2021

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“…In their bootstrapping rule-based approach, they have only counted positive, negative word polarity by SW which is only work for a low limited length text. In Azharul Hasan et al [17], authors proposed a method of using XML based POS tagger and SW to identify the sentiment from Bangla text adopting valency analysis. They have used SW and WordNet (WN) which were designed for only English language.…”

Section: Related Workmentioning

confidence: 99%

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

Bhowmik¹,

Arifuzzaman²,

Mondal³

et al. 2021

NLPRE

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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.

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“…To accomplish this, the machine is required to fathom NLP through training. Hasan et al (2014) [20] have performed sentimental analysis with Bangla text. In spite of acquiring senti-wordnet, the experiment displays difficulty in analysis as the texts are completely in Bangla and not English.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Aspect based Model for Efficient Sentiment Analysis of User Reviews

Priya¹,

Rithika²

2021

CVRJST

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Mining online reviews to extract opinion targets and words are substantial tasks in fine-grained opinion mining. The main aim is to mine sensible multi-grain aspects and opinion words from unlabeled reviews. In this paper, Combined Aspect based Sentiment Model (CASM) is propounded to cooperatively mine multi-grain features and opinions. CASM deals with aspects, opinions, sentiment polarity and granularity concurrently. Support Vector Machine-Radial Basis Function kernel (SVM-RBF) classifier is applied to improve CASM by splitting aspects and opinion words. CASM-SVM-RBF deals with two different kinds of aspects and opinions: general and particular aspects, overall opinions and aspect-precise opinions. Candidates with improved confidence are mined as opinion targets or words. The performance is analyzed in terms of Accuracy, Precision, Recall and F-Score for KNN, BPNN, NBC and ME classifiers.

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Sentiment detection from Bangla text using contextual valency analysis

Cited by 42 publications

References 8 publications

Sentiment Analysis on Bengali Facebook Comments To Predict Fan's Emotions Towards a Celebrity

Sentiment Analysis on Bengali Facebook Comments To Predict Fan's Emotions Towards a Celebrity

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

Intelligent Aspect based Model for Efficient Sentiment Analysis of User Reviews

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