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A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks

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# Effects of Preprocessing in Word Embedding
## *"A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks"*
## *"The Role of Preprocessing for Word Representation Learning in Affective Tasks"*
![frame2a](https://user-images.githubusercontent.com/32373744/80739373-55824a00-8ae4-11ea-88b1-369f855a0821.png)

**Contributions**
1) We conduct a comprehensive analysis of the role of preprocessing techniques in affective tasks (including sentiment analysis, emotion classification and sarcasm detection), employing different models, over nine datasets.
2) We perform a comparative analysis of the accuracy performance of word embedding models when preprocessing is applied at the training phase (training corpora) and/or at the downstream task phase (classification dataset).
3) We evaluate the performance of our best preprocessed word vector model against state-ofthe-art pretrained word embedding models.

**List of Preprocessing Factors:**
1) Punctuation (Punc)
2) Spelling correction (Spell)
3) Stemming (Stem)
4) Stopwords removal (Stop)
5) Negation (Neg)
6) Pos-Tagging (POS)

**Training Models:**
1) Word2Vec (Skip-gram) 
2) Word2Vec (CBOW)
3) BERT (Feature-based Approach)

**Datasets used for Training Word Embeddings:**
1) News : https://www.kaggle.com/snapcrack/all-the-news
2) Wikipedia : https://www.kaggle.com/jkkphys/english-wikipedia-articles-20170820-sqlite

**Datasets used at downstream Tasks for Classification:**

**a) Sentiment Analysis:**
   1) IMDB : http://ai.stanford.edu/ amaas/data/sentiment/
   2) Semeval 2016 : http://alt.qcri.org/semeval2016/task4/index.php
   3) Airlines : https://www.kaggle.com/crowdflower/twitter-airlinesentiment
   4) SST-5: https://nlp.stanford.edu/sentiment/index.html
   
**b) Emotion Detection:**
   1) SSEC: http://www.romanklinger.de/ssec/
   2) ISEAR : https://github.com/PoorvaRane/Emotion-Detector/blob/master/ISEAR.csv
   3) Alm : http://people.rc.rit.edu/~coagla/affectdata/index.html
   
**c) Sarcasm Detection:**
   1) Onion : https://github.com/rishabhmisra/News-Headlines-Dataset-For-Sarcasm-Detection
   2) IAC : https://nlds.soe.ucsc.edu/sarcasm2
   3) Reddit : https://nlp.cs.princeton.edu/SARC/0.0/
   
***Reference:
Nastaran Babanejad, Ameeta Agrawal, Aijun An and Manos Papagelis, A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks, Proceedings of The 58th Annual Meeting of the Association for Computational Linguistics
 (ACL 2020).***

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