Predicting the Sentimental Reviews in Tamil Movie using Machine Learning Algorithms

Se, Shriya; Ravi, Vinayakumar; Kumar, M. Anand; Soman, K P

doi:10.17485/ijst/2016/v9i45/106482

Cited by 31 publications

(6 citation statements)

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“…A probabilistic approach was employed to classify parts of speech (POS) tags [19]. Several research pursuits have been worked upon SA in Tamil [20,21]. A recursive neural network approach was opted to improve the accuracy of texts in Tamil [22].…”

Section: Sentiment Analysismentioning

confidence: 99%

Benchmarking Multi-Task Learning for Sentiment Analysis and Offensive Language Identification in Under-Resourced Dravidian Languages

Hande¹,

Hegde²,

Priyadharshini³

et al. 2021

Preprint

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To obtain extensive annotated data for underresourced languages is challenging, so in this research, we have investigated whether it is beneficial to train models using multi-task learning. Sentiment analysis and offensive language identification share similar discourse properties. The selection of these tasks is motivated by the lack of large labelled data for user-generated code-mixed datasets. This paper works on code-mixed YouTube comments for Tamil, Malayalam, and Kannada languages. Our framework is applicable to other sequence classification problems irrespective of the size of the datasets. Experiments show that our multi-task learning model can achieve high results compared with single-task learning while reducing the time and space constraints required to train the models on individual

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Section: Sentiment Analysismentioning

confidence: 99%

Benchmarking Multi-Task Learning for Sentiment Analysis and Offensive Language Identification in Under-Resourced Dravidian Languages

Hande¹,

Hegde²,

Priyadharshini³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…They employed supervised machine learning techniques such as SVM, naive Bayes, maximum entropy and ANN to classify the Twitter data. Se et al (2016) have proposed a method based on supervised machine learning for classifying the Tamil movie reviews as positive and negative. For analyzing the social media text where the data are increasing exponentially, machine learning algorithms such as SVM, Maxent classifier, decision tree and naive Bayes were used.…”

Section: Related Workmentioning

confidence: 99%

OMLML: a helpful opinion mining method based on lexicon and machine learning in social networks

Keyvanpour

Zandian

Heidarypanah

2020

Soc. Netw. Anal. Min.

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Identification of users' polarities and mining their opinions in various areas, especially social networks, has become one of the popular and useful research fields. Although opinion mining and analyzing methods based on machine learning or lexicon have been useful, high training cost based on time or memory used, lack of enriched and complete lexicons, high dimensions of feature space and ambiguity in positive or negative detection of some sentences in these methods are examples of their downsides. To cope with these problems, in this paper a helpful method based on lexicon and machine learning called OMLML is proposed by using social networks. The main superiority of the proposed method compared to other methods is addressing these challenges simultaneously. According to the proposed method, the polarity of the opinions toward a target word is first determined using a method based on lexicon and textual features of words and sentences. Next, having mapped feature space into a 3-D vector, opinions are analyzed and classified based on a new machine learning method. The results of quantitative and qualitative experiments show that mapping data into a new space decreases training cost and that the performance of the proposed method than is acceptable particularly from the perspective of accuracy, F-measure and runtime.

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“…In the advent of transfer learning, GloVe (Pennington et al, 2014), Word2Vec (Mikolov et al, 2013b), fastText (Bojanowski et al, 2017a) comes with their pros and cons. Malayalam (Nair et al, 2014;Sarkar and Chakraborty, 2015;Se et al, 2015;Se et al, 2016;Mouthami et al, 2013) has official status in India and other countries. Several research activities on sentiment analysis and events are focused on Malayalam due to their population and use of this language.…”

Section: Related Workmentioning

confidence: 99%

A Sentiment Analysis Dataset for Code-Mixed Malayalam-English

Chakravarthi,

Jose,

Suryawanshi

et al. 2020

Preprint

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There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff's alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.

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Predicting the Sentimental Reviews in Tamil Movie using Machine Learning Algorithms

Cited by 31 publications

References 0 publications

Benchmarking Multi-Task Learning for Sentiment Analysis and Offensive Language Identification in Under-Resourced Dravidian Languages

Benchmarking Multi-Task Learning for Sentiment Analysis and Offensive Language Identification in Under-Resourced Dravidian Languages

OMLML: a helpful opinion mining method based on lexicon and machine learning in social networks

A Sentiment Analysis Dataset for Code-Mixed Malayalam-English

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