Fast and Accurate Sentiment Classification Using an Enhanced Naive Bayes Model

Narayanan, Vivek; Arora, Ishan; Bhatia, Arjun

doi:10.1007/978-3-642-41278-3_24

Cited by 192 publications

(116 citation statements)

References 8 publications

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“…Negation permits to change a word's meaning to its inverse meaning [28]. Therefore during the extraction of feature it is essential to represent the process whether or not a word is negated.…”

Section: Challenges Encountered In Sentiment Analysis Of English 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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Section: Challenges Encountered In Sentiment Analysis Of English 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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“…We also considered several advantages of Bayesian classifiers such as ease of implementation, speed in training and classification, and the fact that they can be used for real time prediction [40][41][42].…”

Section: Bayesian Based Classifier For Authenticationmentioning

confidence: 99%

Evaluating smartphone-based dynamic security questions for fallback authentication: a field study

Albayram

Khan

2016

Hum. Cent. Comput. Inf. Sci.

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To address the limitations of static challenge question based fallback authentication mechanisms (e.g., easy predictability), recently, smartphone based autobiographical authentication mechanisms have been explored where challenge questions are not predetermined and are instead generated dynamically based on users’ day-to-day activities captured by smartphones. However, as answering different types and styles of questions is likely to require different amounts of cognitive effort and affect users’ performance, a thorough study is required to investigate the effect of type and style of challenge questions and answer selection mechanisms on users’ recall performance and usability of such systems. Towards that, this paper explores seven different types of challenge questions where different types of questions are generated based on users’ smartphone usage data. For evaluation, we conducted a field study for a period of 30 days with 24 participants who were recruited in pairs to simulate different kinds of adversaries (e.g., close friends, significant others). Our findings suggest that the question types do have a significant effect on user performance. Furthermore, to address the variations in users’ accuracy across multiple sessions and question types, we investigate and present a Bayesian classifier based authentication algorithm that can authenticate legitimate users with high accuracy by leveraging individual response patterns.

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“…www.ijacsa.thesai.org  Negation Handling: Negation allows to alter the meaning of a word to its opposite meaning. Therefore, during the feature extraction, it is important to indicate process whether or not a word is negated [3]. If the negation is not handling the algorithm will understand the opposite meaning of the sentence, and will have less accurate predictions.…”

Section: A General Research Fieldmentioning

confidence: 99%

“…The three main machine learning algorithms which are applied for sentiment analysis are: Naive Bayes [3], Maximum Entropy [4], and Support Vector Machine (SVM) [5]. The accuracy of these three algorithm depends on the feature extraction method which is applied and the analyzed datasets.…”

Section: A General Research Fieldmentioning

confidence: 99%

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A Topic based Approach for Sentiment Analysis on Twitter Data

Ficamos¹,

Yan²

2016

ijacsa

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Abstract-Twitter has grown in popularity during the past decades. It is now used by millions of users who share information about their daily life and their feelings. In order to automatically process and analyze these data, applications can rely on analysis methods such as sentiment analysis and topic modeling. This paper contributes to the sentiment analysis research field. First, the preprocessing steps required to extract features from Twitter data are described. Then, a topic based method is proposed so as to estimate the sentiment of a tweet. This method requires to extract topics from the training dataset, and train models for each of these topics. The method allows to increase the accuracy of the sentiment estimation compared to using a single model for every topic.

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Fast and Accurate Sentiment Classification Using an Enhanced Naive Bayes Model

Cited by 192 publications

References 8 publications

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

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

Evaluating smartphone-based dynamic security questions for fallback authentication: a field study

A Topic based Approach for Sentiment Analysis on Twitter Data

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