Harnessing Twitter for Answering Opinion List Queries

Mullick, Ankan; Goyal, Pawan; Ganguly, Niloy; Gupta, Manish

doi:10.1109/tcss.2018.2881186

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…[4] Samah Aloufi et al proposed a domain specific approach for understanding the sentiment of football fans' during the event. [5] Ankan Mullick et al presented an end-to-end system that identifies the opinion list using the hash tags used on twitter, extracting suitable list answers from relevant tweets. [6] Mark E. Larsen et al describes the "We Feel" for analyzing global and regional variations in emotional expression and report the results of validation against known patterns of variation in mood.…”

Section: IImentioning

confidence: 99%

Real time Sentiment Analysis of Tweets using Apache Spark and Scala

Mareeswari

Patil

Ramanan³

2021

acsjse

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Sentiment Analysis is becoming the field of focus with time considering the user experience weighs much more for the business to grow and for the studies as well. The sentimental expressions refers to the emotions or feeling of a person across certain point of focus or issues. So, in this project, with the assistance of Apache Spark Framework, an open source data streaming and processing platform, sentiment evaluation is done on the tweets from Twitter by the means of real time processing as well as an Ad-hoc Run. Some preprocessing of the textual data has been done upon for better characteristics extraction thus resulting in greater accuracy. The validation of this has been done for achieving better result by comparing the other processes when Naive Bayes algorithm is used.

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Section: IImentioning

confidence: 99%

Real time Sentiment Analysis of Tweets using Apache Spark and Scala

Mareeswari

Patil

Ramanan³

2021

acsjse

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“…The work of Mullick et al [20] presents a technique to extract particular feature from the social network using learning-based technique. Usage of genetic algorithm was seen in the work of Iqbal et al [21] where the focus was over reduction of the feature.…”

Section: A Backgroundmentioning

confidence: 99%

Framework for opinion as a service on review data of customer using semantics based analytics

Rajeshwari¹,

Vagdevi²

2020

IJECE

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At Opinion mining plays a significant role in representing the original and unbiased perception of the products/services. However, there are various challenges associated with performing an effective opinion mining in the present era of distributed computing system with dynamic behaviour of users. Existing approaches is more laborious towards extracting knowledge from the reviews of user which is further subjected to various rounds of operation with complex procedures. The proposed system addresses the problem by introducing a novel framework called as Opinion-as-a-Service which is meant for direct utilization of the extracted knowledge in most user friendly manner. The proposed system introduces a set of three sequential algorithm that performs aggregated of incoming stream of opinion data, performing indexing, followed by applying semantics for extracting knowledge. The study outcome shows that proposed system is better than existing system in mining performance.

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Effective Analysis of Tweets Using Hadoop Ecosystem

Singh

Verma

2021

Lecture Notes in Electrical Engineering

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Harnessing Twitter for Answering Opinion List Queries

Cited by 5 publications

References 36 publications

Real time Sentiment Analysis of Tweets using Apache Spark and Scala

Real time Sentiment Analysis of Tweets using Apache Spark and Scala

Framework for opinion as a service on review data of customer using semantics based analytics

Effective Analysis of Tweets Using Hadoop Ecosystem

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