Sentiment Classification for Hindi Tweets in a Constrained Environment Augmented Using Tweet Specific Features

Venugopalan, Manju; Gupta, Deepa

doi:10.1007/978-3-319-26832-3_63

Cited by 20 publications

(3 citation statements)

References 2 publications

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“…There are various emotion analysers for the English language [4][5][6][7][8], but little work has been done in the context of Indian languages [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. The fundamental reason for this is a scarcity of materials in Indian languages.…”

Section: Literaturementioning

confidence: 99%

“…Venugopalan M, Gupta D [ 6] proposed work explores Sentiment Analysis on Hindi tweets in a constrained environment and hence proposes a model for dealing with the challenges in extracting sentiment from Hindi tweets. They have used raw corpus provided by Indian Languages Corpora Initiative (ILCI) to train the Doc2Vec model and for pre-processing, Doc2Vec tool that gives the semantic representation of a sentence in the dataset.…”

Section: Literaturementioning

confidence: 99%

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A Sentence Level Classification of Telugu News Document using Sentiment Analysis

Bygani,

Venkateshwaralu,

Ramana

2023

E3S Web Conf.

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In recent years, sentiment analysis-based categorization in low-resource languages and regional languages has become a hot topic in natural language processing. Researchers are more interested in categorizing sentiment in Indian languages such as Hindi, Telugu, Tamil, Bengali, Malayalam, and others. To the best of our knowledge, no microscopic study on Indian languages has been published to yet due to a lack of annotated data. Using Telugu sentiment analysis, we presented a two-phase classification technique for Telugu news phrases in this work. It first recognizes subjectivity categorization, which categorizes statements as Positive, Negative, or Neutral. Sentiment Classification is the next step, which divides subjective statements into positive and negative categories. We get an accuracy of 81 percent for sentiment analysis categorization using this method.

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Section: Literaturementioning

confidence: 99%

Section: Literaturementioning

confidence: 99%

A Sentence Level Classification of Telugu News Document using Sentiment Analysis

Bygani,

Venkateshwaralu,

Ramana

2023

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…The authors in [14] used decision tree algorithm for sentiment classification of Hindi tweets under constrained and unconstrained environment and achieved an accuracy of 40.47% and 31.26% respectively. Authors in [15] used SVM and Decision Tree for Hindi tweet in a constrained environment using TF-IDF scores of unigrams and tweet specific features and achieved a test accuracy of 43%. Authors in [16] presented an approach for Classification of Bangla text documents based on inverse class frequency.…”

Section: Machine Learning Techniques Of Sentiment Analysis For Indianmentioning

confidence: 99%

Machine Learning Techniques for Sentiment Analysis of Indian Languages

2019

IJRTE

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Sentiment Analysis is the domain of automatically understanding the emotions, feelings, opinions in a textual data. It is a way of understating how a product, brand, service, idea or an event is viewed by common people, customers and stakeholders. Sentiment Analysis Systems are used by politicians, business leaders, developers and researchers to infer useful information as per their specific needs. It is used in business decision making process to value the views of the customers. Sentiment analysis has become a hot topic of scientific and market research in the field of natural Language Processing. India is a large populated country and the number of Internet users is also huge. Most people share their experience in English. However, during the last decade, due to the accessibility of Internet and evolution in language modelling people express their views in their own native Indian language. With the increase in Indian language text, researchers find it quite fascinating to infer valuable information from this unstructured text data. A number of machine learning techniques have been applied on this textual data set. Basic concepts of Sentiment analysis shall be discussed with focus on Indian language text in this paper. Due to on availability of rich lexicon resources for unsupervised learning techniques and better evaluation measures for the Supervised learning techniques, the later become the first choice for researchers in the field of Natural Language Processing. A comparative analysis shall be made for various supervised machine learning techniques in the context of Indian languages.

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Machine Learning for Opinion and Sector Assessment of ‘Make in India’ Based on User Types

Mridula

Kavitha

2018

Lecture Notes on Data Engineering and Communications Technologies

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Sentiment Classification for Hindi Tweets in a Constrained Environment Augmented Using Tweet Specific Features

Cited by 20 publications

References 2 publications

A Sentence Level Classification of Telugu News Document using Sentiment Analysis

A Sentence Level Classification of Telugu News Document using Sentiment Analysis

Machine Learning Techniques for Sentiment Analysis of Indian Languages

Machine Learning for Opinion and Sector Assessment of ‘Make in India’ Based on User Types

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