Context Specific Lexicon for Hindi Reviews

Mishra, Deepali; Venugopalan, Manju; Gupta, Deepa

doi:10.1016/j.procs.2016.07.283

Cited by 22 publications

(9 citation statements)

References 4 publications

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“…The process of extracting context-aware lexicon work is close to research by [6] but in our research, the extracted lexicon contains words other than noun, adjective, adverb, and verb pos tag class. The polarity score in this section is assigned to the opinion terms derived in the previous section.…”

Section: Context-aware Lexicon Extractionmentioning

confidence: 85%

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Constructing Context-Aware Sentiment Lexicon for Amharic Language

Admasu

Raghavendra

2021

Advances in Intelligent Systems and Computing

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Sentiment lexicon is a crucial tool for determining sentiment polarity toward sentiment words. Manual, Dictionary, and Corpus based are categorized as lexicon-based methods to generate sentiment lexicon. Different researchers have conducted research to generate sentiment lexicon for Amharic language on different domains. However, there is still a gap in this domain to classify and handle contextspecific opinions. In this research, Context-Aware Sentiment lexicon is generated for the Amharic language which is the second most spoken Semitic language next to Arabic, a low resourced and morphologically rich language of Ethiopia. Word2vec approach is applied to generate sentiment lexicon and gives the ability to handle contextual sensitivity terms from the hotel domain. The common evaluation metrics such as recall and precision were used to measure our proposed method's performance. Amharic sentiment lexicons are developed and used in testing the performance of the collected Amharic review text. Finally, this study shows a result of 91.3% recall and a precision of 51.8. Promising results are obtained and comparative experiments have shown that the proposed approach can be utilized to generate lexicon for the Amharic language.

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Section: Context-aware Lexicon Extractionmentioning

confidence: 85%

“…Sentiment lexicon is constructed for various languages such as English, Chinese [5], Hindi [6], Spanish [7], Persia [8], Amharic [9], etc. Sentiment lexicon construction can be done in a manual, dictionary, and corpus based.…”

Section: Literature Reviewmentioning

confidence: 99%

Constructing Context-Aware Sentiment Lexicon for Amharic Language

Admasu

Raghavendra

2021

Advances in Intelligent Systems and Computing

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“…There is lack of adequate tools, annotated corpora and tools for Hindi language. Hindi is one of the mainly spoken languages in the globe [23]. Hindi is written Devanagiri script and the Hindi data is present in electronic form.…”

Section: Hindi Tweetsmentioning

confidence: 99%

A comparative review of the challenges encountered in sentiment analysis of Indian regional language tweets vs English language tweets

Soman¹,

Swaminathan²,

Anandan³

et al. 2018

IJET

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With the developed use of online medium these days for sharing views, sentiments and opinions about products, services, organization and people, micro blogging and social networking sites are acquiring a huge popularity. One of the biggest social media sites namely Twitter is used by several people to share their life events, views and opinion about different areas and concepts. Sentiment analysis is the computational research of reviews, opinions, attitudes, views and peoples' emotions about different products, services, firms and topics through categorizing them as negative and positive emotions. Sentiment analysis of tweets is a challenging task. This paper makes a critical review on the comparison of the challenges associated with sentiment analysis of Tweets in English Language versus Indian Regional Languages. Five Indian languages namely Tamil, Malayalam, Telugu, Hindi and Bengali have been considered in this research and several challenges associated with the analysis of Twitter sentiments in those languages have been identified and conceptualized in the form of a framework in this research through systematic review.

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“…Poems and stories are part of the literature. Stories and poems act as a guide to children about their behavior and manners [2][3] and connect with elders to interconnect ideas and visualize life's opportunities. The use of rhyme and meter gives musical sense to the poetry, which is termed as literary elements whereas stories include a set of incidents and characters.…”

Section: Introductionmentioning

confidence: 99%

“…It is done by abstracting documents and using morphological rules of language. It reduces the time and effort invested in reading the documents [2] [21].…”

Section: Introductionmentioning

confidence: 99%

An Application of Zipf's Law for Prose and Verse Corpora Neutrality for Hindi and Marathi Languages

Bafna¹,

Jatinderkumar²

2020

IJACSA

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Availability of the text in different languages has become possible, as almost all websites have offered multilingual option. Hindi is considered as official language in one of the states of India. Hindi text analysis is dominated by the corpus of stories and poems. Before performing any text analysis token extraction is an important step and supports many applications like text summarization, categorizing text and so on. Token extraction is a part of Natural language processing (NLP). NLP includes many steps such as preprocessing the corpus, lemmatization and so on. In this paper the tokens are extracted by two methods and on two corpora. BaSa, a context-based term extraction technique having different NLP activities, e.g. Term Frequency Inverse Document Frequency (TF-IDF) and Zipf's law are used to count and compare extracted tokens. Further token comparison between both of the methods is achieved. The corpus contains proses and verses of Hindi as well as the Marathi language. Common tokens from corpora of verses and proses of Marathi as well as Hindi are identified to prove that both of them behave same as per as NLP activities are concerned. The betterment of BaSa over Zipf's law is proved. Hindi Corpus includes 820 stories and 710 poems and Marathi corpus includes 610 stories and 505 poems.

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Context Specific Lexicon for Hindi Reviews

Cited by 22 publications

References 4 publications

Constructing Context-Aware Sentiment Lexicon for Amharic Language

Constructing Context-Aware Sentiment Lexicon for Amharic Language

A comparative review of the challenges encountered in sentiment analysis of Indian regional language tweets vs English language tweets

An Application of Zipf's Law for Prose and Verse Corpora Neutrality for Hindi and Marathi Languages

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