Pre-processing of Social Media Posts (Record no. 14806)

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control field 20210324111535.0
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040 ## - CATALOGING SOURCE
Original cataloging agency AIKTC-KRRC
Transcribing agency AIKTC-KRRC
100 ## - MAIN ENTRY--PERSONAL NAME
9 (RLIN) 13861
Author Jan,Rafiya
245 ## - TITLE STATEMENT
Title Pre-processing of Social Media Posts
250 ## - EDITION STATEMENT
Volume, Issue number Vol, 5(1), Jan- Apr
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. New Delhi
Name of publisher, distributor, etc. STM Journals
Year 2018
300 ## - PHYSICAL DESCRIPTION
Pagination 14-18p.
520 ## - SUMMARY, ETC.
Summary, etc. Social media has become a slogan in emotion and sentiment analysis. In today’s era Social media networking sites are almost used by everyone. Social media users share their feelings, thoughts, and experiences with other people by short messages. Short messages are composed of emoticons, slangs, noises, irrelevancies and words. Thus, preprocessing becomes the challenging task for Sentiment analysis. This experiment is performed to evaluate the impact of pre-processing on social data for sentiment classification particularly for slang words. This paper focused on identification of important slang words and to evaluate their impact on sentiment analysis of social media posts. The proposed scheme collects bigrams, trigrams of slang and exploits different features for better results of sentiment classification. N-grams are used for bindings and conditional random fields (CRF) to determine the importance of slang words. Experiments declare that this proposed scheme increases the accuracy of Sentiment analysis.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4622
Topical term or geographic name entry element Computer Engineering
700 ## - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 10500
Co-Author Khan, Afaq Alam
773 0# - HOST ITEM ENTRY
Title Recent trends in programming languages
Place, publisher, and date of publication Noida STM Journals
856 ## - ELECTRONIC LOCATION AND ACCESS
URL http://computers.stmjournals.com/index.php?journal=RTPL&page=article&op=view&path%5B%5D=1585
Link text Click Here
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Koha item type Articles Abstract Database
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          School of Engineering & Technology School of Engineering & Technology Archieval Section 2021-03-24 2021-2021783 2021-03-24 2021-03-24 Articles Abstract Database
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