Action Rules for Sentiment Analysis on Twitter Data Using Spark

Ranganathan, Jaishree; Irudayaraj, Allen S.; Tzacheva, Angelina A.

doi:10.1109/icdmw.2017.14

Cited by 11 publications

(9 citation statements)

References 18 publications

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“…For the experimentation purpose, more than 100 unstructured consumer review text commands with different size taken from online related to mobile phone [21] and shown 12 commands only in the Table 1. The Table 1 shows that the contents of unstructured user text commands for , and number of words belong in the each individual text command, where is the unstructured exiting user text command set, denotes the number of user commands belongs to the input command set and represents the i th user command in the input command set.…”

Section: Results and Di̇scussi̇onsmentioning

confidence: 99%

“…Spark system cautions fast compared to using meta action generating methods implemented by Hadoop MapReduce. The authors Trupthi et al [8] reported an automatic system based on interactive approach that used to predict the sentiments of the reviewers or tweets presented by people in social networks using Bigdata tools like hadoo. A comprehensive method is required to predict sentiment polarity, which supports to promote marketing and concentrate on sentimental analysis techniques, Sentiment classification based on feature and summarizing the opinion.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Type of Supervised Text Classification System for Unstructured Text Comments using Probability Theory Technique

2019

IJRTE

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In this paper, an improved sentimental text analysis system called Probability Based Text Classifier (PBTC) is presented. It aims to train the existing unstructured text command set and to classify the sampled text command belongs into positive or negative polarity based on probability theory and supervised concepts. It consists of three stages pre-processed, training and classification. In the first stage, the proposed (PBTC) system identifies the relevant and irrelevant words in the unstructured text command set based on pre-determined text pattern model. In the second stage it identifies two dissimilar classes over the preprocessed text command set based on predetermined text pattern model and simple probability theory concepts. Next stage, the PBTC identifies the sample test text command without class label belong on which class based on Naive Bayer scheme and trained existing text command set. Experimental result shows that the proposed (PBTC) system is well suitable to train the unstructured text command set and classify the new text command belongs into positive or negative polarity with higher accuracy

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Section: Results and Di̇scussi̇onsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Type of Supervised Text Classification System for Unstructured Text Comments using Probability Theory Technique

2019

IJRTE

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show abstract

“…For example, for customer care services, recommendation systems for online shopping, or smart phones that are able to recognize human emotions, Emotion altering Actionable Patterns include: suggesting calming music, playing mood enhancing movie, changing the background colors to suiting ones, or calling caring friends (for smart phones). In [20] the primary intent of the Action Rules generated is to provide viable suggestions on how to make a twitter user feel more positive. For Twitter social network data, Actionable Recommendations may include -how to increase user's friends count, how to increase the user's follower's count, and how to change the overall sentiment from negative to positive, or from neutral to positive.…”

Section: Actionable Pattern Mining and Applicationsmentioning

confidence: 99%

Emotion Mining in Social Media Data

Ranganathan

Tzacheva

2019

Computer Science &Amp; Information Technology (CS &Amp; IT )

Self Cite

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Emotions are known to influence the perception of human beings along with their memory, thinking and imagination. Human perception is important in today's world in a wide range of factors including but not limited to business, education, art, and music. Microblogging and Social networking sites like Twitter, Facebook are challenging sources of information that allow people to share their feelings and thoughts on a daily basis. In this paper we propose an approach to automatically detect emotions on Twitter messages that explores characteristics of the tweets and the writer's emotion using Support Vector Machine LibLinear model and achieve 98% accuracy. Emotion mining gained attraction in the field of computer science due to the vast variety of systems that can be developed and promising applications like remote health care system, customer care services, smart phones that react based on users's emotion, vehicles that sense emotion of the driver. These emotions help understand the current state of user. In order to perform suitable actions or provide suggestions on how user's can enhance their feeling for a better healthy lifestyle we use actionable recommendations. In this work we extract action rules with respect to the user emotions that help provide suggestions for user's.

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“…Enhancement in seen as per the simulation results achieved through these experiments. Jaishree Ranganathan, et.al (2017) proposed a novel Spark system which utilized Specific Action Rule discovery based on Grabbing Strategy (SARGS) within its implementation [7]. The complete Action Rules such as system DEAR, ARED and Association Action Rules are extracted with the help of this proposed system.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment Analysis Approach Based N-Gram and KNN Classifier

Tripathi

Singh²

2018

ijarcs

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Abstract:The sentiment analysis is the approach which is design to analysis positive, negative and neural aspects towards any approach. In the past years, many techniques are designed for the sentiment analysis of twitter data. Based on the previous study about sentiment analysis, novel approach is presented in this research paper for the sentiment analysis of twitter data. The proposed approach is the combination of feature extraction and classification techniques. The N-gram algorithm is applied for the feature extraction and KNN classifier is applied to classify input data into positive, negative and neural classes. To validate the proposed system, performance is analyzed in terms of precision, recall and accuracy. The experiments results of proposed system show that it performs well as compared to existing system which is based on SVM classifier.

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Action Rules for Sentiment Analysis on Twitter Data Using Spark

Cited by 11 publications

References 18 publications

Type of Supervised Text Classification System for Unstructured Text Comments using Probability Theory Technique

Type of Supervised Text Classification System for Unstructured Text Comments using Probability Theory Technique

Emotion Mining in Social Media Data

Sentiment Analysis Approach Based N-Gram and KNN Classifier

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