| 000 | a | ||
|---|---|---|---|
| 999 |
_c14806 _d14806 |
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| 003 | OSt | ||
| 005 | 20210324111535.0 | ||
| 008 | 210324b xxu||||| |||| 00| 0 eng d | ||
| 040 |
_aAIKTC-KRRC _cAIKTC-KRRC |
||
| 100 |
_913861 _aJan,Rafiya |
||
| 245 | _aPre-processing of Social Media Posts | ||
| 250 | _aVol, 5(1), Jan- Apr | ||
| 260 |
_aNew Delhi _bSTM Journals _c2018 |
||
| 300 | _a14-18p. | ||
| 520 | _aSocial 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 |
_94622 _aComputer Engineering |
|
| 700 |
_910500 _aKhan, Afaq Alam |
||
| 773 | 0 |
_tRecent trends in programming languages _dNoida STM Journals |
|
| 856 |
_uhttp://computers.stmjournals.com/index.php?journal=RTPL&page=article&op=view&path%5B%5D=1585 _yClick Here |
||
| 942 |
_2ddc _cAR |
||