Twitter Sentiment Analysis using Distributed Word and Sentence Representation Article Swipe

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Dwarampudi Mahidhar Reddy , N. V. Subba Reddy ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1904.12580

An important part of the information gathering and data analysis is to find out what people think about, either a product or an entity. Twitter is an opinion rich social networking site. The posts or tweets from this data can be used for mining people's opinions. The recent surge of activity in this area can be attributed to the computational treatment of data, which made opinion extraction and sentiment analysis easier. This paper classifies tweets into positive and negative sentiments, but instead of using traditional methods or preprocessing text data here we use the distributed representations of words and sentences to classify the tweets. We use Long Short Term Memory (LSTM) Networks, Convolutional Neural Networks (CNNs) and Artificial Neural Networks. The first two are used on Distributed Representation of words while the latter is used on the distributed representation of sentences. This paper achieves accuracies as high as 81%. It also suggests the best and optimal ways for creating distributed representations of words for sentiment analysis, out of the available methods.

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Computer Science

Artificial Intelligence

Social Media

Convolutional Neural Network

Concepts

Sentiment analysis Computer science Sentence Preprocessor Natural language processing Representation (politics) Artificial intelligence Social media Word (group theory) Convolutional neural network Data pre-processing Microblogging Product (mathematics) Artificial neural network Information retrieval World Wide Web Linguistics Law Mathematics Political science Politics Philosophy Geometry

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/1904.12580
PDF: https://arxiv.org/pdf/1904.12580
OA Status: green
Cited By: 3
References: 13
Related Works: 20
OpenAlex ID: https://openalex.org/W2941866985

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DOI: https://doi.org/10.48550/arxiv.1904.12580

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Title: Twitter Sentiment Analysis using Distributed Word and Sentence Representation

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Type: preprint

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Language: en

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Publication year: 2019

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Publication date: 2019-04-01

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Authors: Dwarampudi Mahidhar Reddy, N. V. Subba Reddy

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Landing page: https://arxiv.org/abs/1904.12580

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PDF URL: https://arxiv.org/pdf/1904.12580

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Concepts: Sentiment analysis, Computer science, Sentence, Preprocessor, Natural language processing, Representation (politics), Artificial intelligence, Social media, Word (group theory), Convolutional neural network, Data pre-processing, Microblogging, Product (mathematics), Artificial neural network, Information retrieval, World Wide Web, Linguistics, Law, Mathematics, Political science, Politics, Philosophy, Geometry

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Cited by: 3

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Citations by year (recent): 2022: 1, 2021: 1, 2020: 1

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References (count): 13

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Related works (count): 20

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abstract_inverted_index.first	121
abstract_inverted_index.paper	72, 142
abstract_inverted_index.posts	33
abstract_inverted_index.site.	31
abstract_inverted_index.surge	48
abstract_inverted_index.think	16
abstract_inverted_index.using	83
abstract_inverted_index.which	63
abstract_inverted_index.while	130
abstract_inverted_index.words	97, 129, 162
abstract_inverted_index.(CNNs)	115
abstract_inverted_index.(LSTM)	110
abstract_inverted_index.Memory	109
abstract_inverted_index.Neural	113, 118
abstract_inverted_index.about,	17
abstract_inverted_index.either	18
abstract_inverted_index.latter	132
abstract_inverted_index.mining	43
abstract_inverted_index.people	15
abstract_inverted_index.recent	47
abstract_inverted_index.social	29
abstract_inverted_index.tweets	35, 74
abstract_inverted_index.Twitter	24
abstract_inverted_index.easier.	70
abstract_inverted_index.entity.	23
abstract_inverted_index.instead	81
abstract_inverted_index.methods	85
abstract_inverted_index.opinion	27, 65
abstract_inverted_index.optimal	155
abstract_inverted_index.product	20
abstract_inverted_index.tweets.	103
abstract_inverted_index.Networks	114
abstract_inverted_index.achieves	143
abstract_inverted_index.activity	50
abstract_inverted_index.analysis	9, 69
abstract_inverted_index.classify	101
abstract_inverted_index.creating	158
abstract_inverted_index.methods.	170
abstract_inverted_index.negative	78
abstract_inverted_index.people's	44
abstract_inverted_index.positive	76
abstract_inverted_index.suggests	151
abstract_inverted_index.Networks,	111
abstract_inverted_index.Networks.	119
abstract_inverted_index.analysis,	165
abstract_inverted_index.available	169
abstract_inverted_index.gathering	6
abstract_inverted_index.important	1
abstract_inverted_index.opinions.	45
abstract_inverted_index.sentences	99
abstract_inverted_index.sentiment	68, 164
abstract_inverted_index.treatment	60
abstract_inverted_index.Artificial	117
abstract_inverted_index.accuracies	144
abstract_inverted_index.attributed	56
abstract_inverted_index.classifies	73
abstract_inverted_index.extraction	66
abstract_inverted_index.networking	30
abstract_inverted_index.sentences.	140
abstract_inverted_index.Distributed	126
abstract_inverted_index.distributed	94, 137, 159
abstract_inverted_index.information	5
abstract_inverted_index.sentiments,	79
abstract_inverted_index.traditional	84
abstract_inverted_index.Convolutional	112
abstract_inverted_index.computational	59
abstract_inverted_index.preprocessing	87
abstract_inverted_index.Representation	127
abstract_inverted_index.representation	138
abstract_inverted_index.representations	95, 160
cited_by_percentile_year
countries_distinct_count	1
institutions_distinct_count	2
citation_normalized_percentile