SeNTU: Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised Learning

Chikersal, Prerna; Poria, Soujanya; Wang, Zhaoxia

doi:10.18653/v1/s15-2108

Cited by 106 publications

(37 citation statements)

References 16 publications

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“…In both works, Lexicons of Emoticons are used to enhance the quality of the results. Authors in [29] propose a system that uses an SVM (Support Vector Machine) classifier alongside a rule-based classifier so as to improve the accuracy of the system. In [30], the authors proceed with a two-step classification process.…”

Section: Sentiment Analysis and Classification Modelsmentioning

confidence: 99%

Large Scale Implementations for Twitter Sentiment Classification

et al. 2017

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Sentiment Analysis on Twitter Data is indeed a challenging problem due to the nature, diversity and volume of the data. People tend to express their feelings freely, which makes Twitter an ideal source for accumulating a vast amount of opinions towards a wide spectrum of topics. This amount of information offers huge potential and can be harnessed to receive the sentiment tendency towards these topics. However, since no one can invest an infinite amount of time to read through these tweets, an automated decision making approach is necessary. Nevertheless, most existing solutions are limited in centralized environments only. Thus, they can only process at most a few thousand tweets. Such a sample is not representative in order to define the sentiment polarity towards a topic due to the massive number of tweets published daily. In this work, we develop two systems: the first in the MapReduce and the second in the Apache Spark framework for programming with Big Data. The algorithm exploits all hashtags and emoticons inside a tweet, as sentiment labels, and proceeds to a classification method of diverse sentiment types in a parallel and distributed manner. Moreover, the sentiment analysis tool is based on Machine Learning methodologies alongside Natural Language Processing techniques and utilizes Apache Spark's Machine learning library, MLlib. In order to address the nature of Big Data, we introduce some pre-processing steps for achieving better results in Sentiment Analysis as well as Bloom filters to compact the storage size of intermediate data and boost the performance of our algorithm. Finally, the proposed system was trained and validated with real data crawled by Twitter, and, through an extensive experimental evaluation, we prove that our solution is efficient, robust and scalable while confirming the quality of our sentiment identification.

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Section: Sentiment Analysis and Classification Modelsmentioning

confidence: 99%

Large Scale Implementations for Twitter Sentiment Classification

et al. 2017

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“…al. [3] developed by Rule-based Classifier combining with Supervised Learning, used the rule-based classifier which is based on rules that are dependent on the occurrences of featured keywords and polarity in tweets. Whereas, the Support Vector Machine (SVM) is trained on semantic, dependency, and sentiment lexicon based features.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis using Averaged Histogram

Pal¹,

Ghosh²

2017

IJCA

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“…Whereas, the Support Vector Machine (SVM) is trained on semantic, dependency, and sentiment lexicon based features. The tweets are classified as positive, negative or unknown by the rule-based classifier, and as positive, negative or neutral by the SVM [3]. BogdonBatrinca, Philip C. Tr.eleaven, (2014),states an overview of software tool for social media, blogs, chats, newsfeeds etc.…”

Section: Introduction Social Mediamentioning

confidence: 99%

Implementation of Feature Selection and balanced random forest for Sentimental Analysis of Text Databases

2017

IJIET

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Our reviews and the assessments of others play a vital part in our basic leadership process also, even impact our conduct. As of late, an expanding number of individuals have taken to communicating their conclusions on a wide assortment of subjects. With innovation's expanding capacities, sentiment analysis turns into a more used apparatus for organizations. Social media monitoring tools utilize it to give their clients bits of knowledge about how the general population feels as to their business, items, or subjects of intrigue. It's broadly utilized by email administrations to keep spam away from your inbox and by survey sites to suggest new substance like movies or TV shows. When somebody writings you with a snide remark (without emoticons) would you be able to tell if it's snide? In the event that they're really cheerful, furious or nonpartisan? That is the thing that makes sentiment analysis such a broad and intriguing field. Sentiment analysis-additionally called conclusion mining-is the way toward characterizing and ordering suppositions in a given bit of content as positive, negative, or impartial. In this paper, we have proposed Correlation based feature selection algorithm with random forest classification. The proposed technique has been tested on various text datasets and the experimental results shown that proposed technique performs better than the existing classification technique.

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SeNTU: Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised Learning

Cited by 106 publications

References 16 publications

Large Scale Implementations for Twitter Sentiment Classification

Large Scale Implementations for Twitter Sentiment Classification

Sentiment Analysis using Averaged Histogram

Implementation of Feature Selection and balanced random forest for Sentimental Analysis of Text Databases

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