Sentiment analysis using Telugu SentiWordNet

Naidu, R. Subramanyam; Bharti, Santosh Kumar; Babu, Korra Sathya; Mohapatra, Ramesh Kumar

doi:10.1109/wispnet.2017.8299844

Cited by 30 publications

(15 citation statements)

References 8 publications

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“…The accuracy difference between the systems was because of the translation carried out into the English language [10]. Naidu et al [11] carried out the SA on Telugu e-newspapers. Their system was divided in two steps.…”

Section: Sentence Levelmentioning

confidence: 99%

“…In the second step only, the subjective sentences were further classified as positive, negative and neutral. Both the steps were performed using the SWN which gave the accuracy of 74% and 81% [11].…”

Section: Sentence Levelmentioning

confidence: 99%

“…Researchers in [20] collected 3000 positive and 3000 negative Odia movie reviews. Authors in [11] collected 1400 Telugu sentences from e-Newspapers from data 1st December 2016 to 31st December 2016.…”

Section: Datasetsmentioning

confidence: 99%

“…In some paper's lexicon-based approach was used to carry out the classification task [10,11,13,15,19,21]. SWN's of different languages were created and improved to carry out the task effectively.…”

Section: Classification Techniquesmentioning

confidence: 99%

See 3 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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Section: Sentence Levelmentioning

confidence: 99%

Section: Sentence Levelmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Classification Techniquesmentioning

confidence: 99%

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

Shah

Kaushik

2019

Preprint

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“…The primary task in sentiment analysis is to identify the polarity of text in any given document. The polarity may be either positive, negative or neutral (Naidu et al, 2017). Sentiment is a property of human intelligence and is not entirely based on the features of a language.…”

Section: Building the Benchmark Corpusmentioning

confidence: 99%

BCSAT : A Benchmark Corpus for Sentiment Analysis in Telugu Using Word-level Annotations

Parupalli¹,

Rao

Mamidi

2018

Proceedings of ACL 2018, Student Research Workshop

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The presented work aims at generating a systematically annotated corpus that can support the enhancement of sentiment analysis tasks in Telugu using wordlevel sentiment annotations. From On-toSenseNet, we extracted 11,000 adjectives, 253 adverbs, 8483 verbs and sentiment annotation is being done by language experts. We discuss the methodology followed for the polarity annotations and validate the developed resource. This work aims at developing a benchmark corpus, as an extension to SentiWordNet, and baseline accuracy for a model where lexeme annotations are applied for sentiment predictions. The fundamental aim of this paper is to validate and study the possibility of utilizing machine learning algorithms, word-level sentiment annotations in the task of automated sentiment identification. Furthermore, accuracy is improved by annotating the bi-grams extracted from the target corpus. * * This work was presented at Student Research Workshop in 56 th Annual Meeting of the Association for Computational Linguistics, ACL.

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