Unsupervised method for sentiment analysis in online texts

Fernández-Gavilanes, Milagros; Álvarez-López, Tamara; Juncal-Martínez, Jonathan; Costa-Montenegro, Enrique; González-Castaño, Francisco J.

doi:10.1016/j.eswa.2016.03.031

Cited by 146 publications

(48 citation statements)

References 25 publications

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“…(RNNLM) have only begun to attract a significant amount of attention in recent years [24,25,26,27], along with other text classification tasks, such as sentiment analysis [28,22,29,30] or fake-news detection [31,32,33]. Training an LM with an RNN as its sequence model paradigm enables models to be created that reflect a target word's long and varied history.…”

Section: Deep Language Modelingmentioning

confidence: 99%

Choosing the right word: Using bidirectional LSTM tagger for writing support systems

Makarenkov

Rokach

Shapira

2019

Engineering Applications of Artificial Intelligence

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Scientific writing is difficult. It is even harder for those for whom English is a second language (ESL learners). Scholars around the world spend a significant amount of time and resources proofreading their work before submitting it for review or publication.In this paper we present a novel machine learning based application for proper word choice task. Proper word choice is a generalization the lexical substitution (LS) and grammatical error correction (GEC) tasks. We demonstrate and evaluate the usefulness of applying bidirectional Long Short Term Memory (LSTM) tagger, for this task. While state-of-the-art grammatical error correction uses error-specific classifiers and machine translation methods, we demonstrate an unsupervised method that is based solely on a high quality text corpus and does not require manually annotated data. We use a bidirectional Recurrent Neural Network (RNN) with LSTM for learning the proper word choice based on a word's sentential context. We demonstrate and evaluate our application on both a domain-specific (scientific), writing task and a general-purpose writing task. We show that our domain-specific and general-purpose models outperform state-of-the-art general context learning. As an additional contribution of this research, we also share our code, pretrained models, and a new ESL learner test set with the research community.

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Section: Deep Language Modelingmentioning

confidence: 99%

Choosing the right word: Using bidirectional LSTM tagger for writing support systems

Makarenkov

Rokach

Shapira

2019

Engineering Applications of Artificial Intelligence

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“…Twitter with 500 million users has turn into a great source to discover the user's opinion, emotions and feelings about services, products, political problems, or any other issues and possibly to create a framework to deal with it in future. Twitter gives users the ability to share their opinion in a short-term message with maximum 140 characters [1].…”

Section: Introductionmentioning

confidence: 99%

Feature-based Sentiment Analysis for Slang Arabic Text

Abdallah¹,

Abo-Suaileek²

2019

IJACSA

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The increased number of Arab users on microblogging services who use Arabic language to write and read has triggered several researchers to study the posted data and discover the user's opinion and feelings to support decision making. In this paper, a sentiment analysis framework is presented for slang Arabic text. A new dataset with Jordanian dialect is presented. Numerous specific Arabic features are shown with their impact on slang Arabic Tweets. The new set of features consists of lexicon, writing style, grammatical and emotional features. Several experiments are conducted to test the performance of the proposed scheme. The new proposed scheme produces better results in comparison with others. The experiments show that the system performs well without translating the tweets to English or standard Arabic.

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“…Unsupervised parsing based polarity determination which combines natural language processing and sentiment features from sentiment lexicons are presented [6]. This approach is compared with baseline algorithms that determine positive, negative and neutral opinion.…”

Section: Dictionary Based Approachmentioning

confidence: 99%

“…A methodology is proposed for sentiment propagation only. In order to improve the classification performance, it requires linguistic patterns on different domain [6]. A methodology based on word level and sentence level sentiment analysis on building lexicon level sentiment classification is discussed.…”

Section: Open Issues and Research Gapsmentioning

confidence: 99%

Perspectives of the performance metrics in lexicon and hybrid based approaches: a review

Rani¹,

Sumathy²

2017

IJET

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Online social media and social networking services experience a drastic development in the present scenario. Contents generated by hundreds of millions of users are used for communication in general. Users mark their opinion and review in various applications such as Twitter, Facebook, YouTube, Weibo, Flicker, LinkedIn, Online-e commerce sites, Microblogging sites, etc. User generated text is spread rapidly on the web, and it has become tedious to analyze the opinionated text in order to arrive at a decision. Sentiment analysis, a subcategory of text mining is the major active research domain in current era due to greater quantity of opinionated text present in the Internet. Semantic detection is the sub-class in the sentiment analysis which is used for measuring the sentiment orientation in any text. Opinionated text is used for analyzing and making the decision simple. This interdisciplinary field draws various techniques from data mining, machine learning, natural language processing, lexicon based and hybrid based approaches. This paper provides a broad perspective with the highlight of the current state-of art techniques emphasizing the various research challenges and gaps present. The performance metrics in terms of detection rate, precision, recall, f-measure/score, average mean, auto-Pearson correlation, cosine similarity and ratio of time on various algorithms is discussed in detail. An analysis of the text mining approaches in different domains is presented.

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Unsupervised method for sentiment analysis in online texts

Cited by 146 publications

References 25 publications

Choosing the right word: Using bidirectional LSTM tagger for writing support systems

Choosing the right word: Using bidirectional LSTM tagger for writing support systems

Feature-based Sentiment Analysis for Slang Arabic Text

Perspectives of the performance metrics in lexicon and hybrid based approaches: a review

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