Phrase-level sentence patterns for estimating positive and negative emotions using Neuro-fuzzy model for information retrieval applications

Prasanna, M. S. M.; Shaila, S. G.; Vadivel, A.

doi:10.1007/s11042-020-10422-6

Cited by 5 publications

(1 citation statement)

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“…Prasanna et al [18] had introduced neuro-fuzzy structure-based phrase-level emotion patterns. In the approach, the patterns were extracted at the level of phrase and converted into fuzzy rules for emotion classification.…”

Section: Related Workmentioning

confidence: 99%

Twitter Data Analysis Using Hadoop and ‘R’ and Emotional Analysis Using Optimized SVNN

Kumar¹,

Manoj²,

Geetha³

2023

Computer Systems Science and Engineering

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Standalone systems cannot handle the giant traffic loads generated by Twitter due to memory constraints. A parallel computational environment provided by Apache Hadoop can distribute and process the data over different destination systems. In this paper, the Hadoop cluster with four nodes integrated with RHadoop, Flume, and Hive is created to analyze the tweets gathered from the Twitter stream. Twitter stream data is collected relevant to an event/topic like IPL-2015, cricket, Royal Challengers Bangalore, Kohli, Modi, from May 24 to 30, 2016 using Flume. Hive is used as a data warehouse to store the streamed tweets. Twitter analytics like maximum number of tweets by users, the average number of followers, and maximum number of friends are obtained using Hive. The network graph is constructed with the user's unique screen name and mentions using 'R'. A timeline graph of individual users is generated using 'R'. Also, the proposed solution analyses the emotions of cricket fans by classifying their Twitter messages into appropriate emotional categories using the optimized support vector neural network (OSVNN) classification model. To attain better classification accuracy, the performance of SVNN is enhanced using a chimp optimization algorithm (ChOA). Extracting the users' emotions toward an event is beneficial for prediction, but when coupled with visualizations, it becomes more powerful. Bar-chart and wordcloud are generated to visualize the emotional analysis results.

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Section: Related Workmentioning

confidence: 99%