Sentiment Analysis of Movie Reviews in Hindi Language Using Machine Learning

Nanda, Charu; Dua, Mohit; Nanda, Garima

doi:10.1109/iccsp.2018.8524223

Cited by 52 publications

(16 citation statements)

References 6 publications

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“…Nanda et al [12] used the SWN to automatically annotate the movie review dataset. Machine learning algorithms Random Forest and SVM were used to carry out the sentiment classification.…”

Section: Sentence Levelmentioning

confidence: 99%

“…Random Forest performed better than SVM giving the accuracy of 91%. Performance metrics used to evaluate the algorithms were accuracy, precision, recall, F1-score [12].…”

Section: Sentence Levelmentioning

confidence: 99%

“…Some researchers have extracted their data from Twitter [10,17,18,21,24,25,26], while some have opted for extracting the data manually or by performing web scrapping on different websites like Facebook, microblogs, e-commerce websites, YouTube etc. [8,9,12,13,15,23]. Authors in [14] have accessed the FIRE 2015 dataset.…”

Section: Datasetsmentioning

confidence: 99%

“…In various studies machine learning algorithms were used to carry out the SA task [8,9,12,17,20,22,23]. It was observed that SVM performed very well for the sentiment classification followed by LR and Naïve Bayes algorithm.…”

Section: Classification Techniquesmentioning

confidence: 99%

“…Ontology was also manually created which can affect the accuracy of the system. In [12] the data was classified based on only 2 sentiments positive and negative. Neutral polarity was not considered which could affect the analysis to greater extent.…”

Section: Challenges and Limitationsmentioning

confidence: 99%

See 4 more Smart Citations

Sentiment Analysis on Indian Indigenous Languages: A Review on Multilingual Opinion Mining

Shah

Kaushik

2019

Preprint

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An increase in the use of smartphones has laid to the use of the internet and social media platforms. The most commonly used social media platforms are Twitter, Facebook, WhatsApp and Instagram. People are sharing their personal experiences, reviews, feedbacks on the web. The information which is available on the web is unstructured and enormous. Hence, there is a huge scope of research on understanding the sentiment of the data available on the web. Sentiment Analysis (SA) can be carried out on the reviews, feedbacks, discussions available on the web. There has been extensive research carried out on SA in the English language, but data on the web also contains different other languages which should be analyzed. This paper aims to analyze, review and discuss the approaches, algorithms, challenges faced by the researchers while carrying out the SA on Indigenous languages.

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“…Nanda et al [12] used the SWN to automatically annotate the movie review dataset. Machine learning algorithms Random Forest and SVM were used to carry out the sentiment classification.…”

Section: Sentence Levelmentioning

confidence: 99%

“…Random Forest performed better than SVM giving the accuracy of 91%. Performance metrics used to evaluate the algorithms were accuracy, precision, recall, F1-score [12].…”

Section: Sentence Levelmentioning

confidence: 99%