SlowGuess/ACL_2020_OCR huggingface dataset:
The optimizer is Adam Kingma and Ba (2014), all loss functions are sample-wise, and we take the mean of all samples (epoch = 5, 10, batch size = 64, 128). All sentiment and sarcasm datasets are split into training/testing using  $80\% / 20\%$ , with  $10\%$  validation from training. For the smaller and imbalanced emotion datasets, we use stratified 5-fold cross-validation. We use a dropout layer to prevent overfitting by ignoring randomly selected neurons during training. We use early stopping when validation loss stops improving with patience  $= 3$ , min-delta  $= 0.0001$ . The results are reported in terms of weighted F-score (as some emotion datasets are highly imbalanced), where F-score  $= 2\frac{p \cdot r}{p + r}$ , with  $p$  denoting precision, and  $r$  is recall.
5 Discussion and Analysis
We analyze the impact of preprocessing techniques in word representation learning on affect analysis.
5.1 Effect of Preprocessing Factors
A primary goal of this work is to identify the most effective preprocessing factors for training word embeddings for affective tasks. Table 4 details the results of our experiments comparing the performance of individual preprocessing factors as well as those of ablation studies (i.e., including all the factors but one).
Observing the performance of the individual factors on the News corpus, we note that even a single simple preprocessing technique can bring improvements, thereby validating our intuition of incorporating preprocessing into training corpora of word representations. Second, negation (neg) processing appears to be consistently the most
<sup>13</sup>https://github.com/rishabhmisra/News-Headlines-Dataset-For-Sarcasm-Detection
$^{14}$ https://nlds.soe.ucsc.edu/sarcasm2
15SARC v0.0: https://nlp.cs.princeton.edu/SARC/0.0/
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Models	Processing	IMDB	Semeval	Airline	IAC	Onion	Reddit	Alm	ISEAR	SSEC
CBOW	Basic	83.99	55.69	60.73	65.74	68.23	59.42	36.81	55.43	51.76
stop	84.43	55.72	61.37	66.03	68.17	59.27	36.81	56.01	52.33
spell	86.20	55.93	61.96	66.00	69.57	60.00	36.88	56.41	52.14
stem	86.92	55.72	61.86	65.89	68.49	59.72	36.94	55.84	51.89
punc	86.99	56.41	62.08	65.93	69.85	60.28	36.94	56.89	52.03
pos	85.66	56.83	62.75	66.32	70.25	60.63	37.02	57.04	53.19
neg	88.98	57.29	63.81	66.87	71.12	60.91	37.22	57.39	54.15
All	89.96	57.82	64.58	67.23	70.90	60.84	37.43	57.72	53.71
All - neg	84.67	55.00	61.58	66.02	69.73	59.94	36.91	55.89	51.94
All - pos	85.69	56.31	64.29	66.97	70.48	60.15	37.19	56.27	52.16
All - punc	86.41	56.88	63.01	66.75	70.01	60.00	37.01	57.19	52.43
All - spell	88.23	56.41	63.87	67.23	70.83	60.27	37.22	57.41	53.41
All - stop	90.01	60.82	66.84	67.20	72.49	62.11	38.96	59.28	55.00
All - stem	88.12	60.82	67.12	69.25	72.13	61.73	38.00	59.00	55.42
Skip-gram	Basic	83.07	54.23	61.47	65.51	68.01	59.75	35.87	55.64	51.49
stop	83.23	55.47	62.00	65.62	68.00	59.84	35.94	55.76	51.62
spell	85.90	55.48	62.00	65.61	69.76	60.28	36.10	55.93	52.30
stem	86.00	55.33	61.89	65.60	68.72	59.50	36.00	55.69	51.40
punc	86.68	55.79	62.38	65.89	70.00	60.44	36.41	56.81	52.71
pos	85.91	56.28	63.25	66.24	69.81	60.85	36.44	56.23	52.94
neg	87.28	56.89	63.72	66.87	70.59	61.27	36.87	57.34	53.10
All	88.36	57.04	64.91	66.94	70.73	61.12	37.10	57.92	53.58
All - neg	83.26	54.00	61.95	66.00	69.88	60.00	36.94	55.97	51.89
All - pos	86.21	55.22	65.12	66.06	69.88	61.00	37.00	56.42	52.10
All - punc	85.57	55.99	64.29	66.29	70.00	60.98	37.01	57.02	52.53
All - spell	86.00	56.98	65.00	66.25	70.25	0.61	37.04	57.69	52.86
All - stop	88.74	60.93	67.00	68.57	72.20	62.02	38.92	59.18	55.18
All - stem	88.42	60.67	67.39	69.08	72.00	62.36	37.44	59.48	55.23
Table 4: F-score results of evaluating the effect of preprocessing factors using CBOw and Skip-gram on News corpus. The overall best results are in bold. The best result using only any one preprocessing setting is underlined.
effective factor across all the 9 datasets, indicating its importance in affective classification, followed by parts-of-speech (pos) processing where we retained words belonging only to one of four classes. On the other hand, removing stopwords (stop), spellchecking (spell) and stemming (stem) yield little improvement and mixed results. Interestingly, applying all the preprocessing factors is barely better or in some cases even worse (Onion, Reddit and SSEC) than applying just negation. Finally, the best performance comes from combining all the preprocessing factors except stemming (All-stem). Moreover, Table 5 details the performance of ablation studies on Wikipedia corpus for all three models where we note that the best performance for the CBOw model comes from combining all the preprocessing factors except stemming (All-stem), whereas for the Skip-gram and BERT models, the best results are obtained by applying all the preprocessing factors except stopwords removal (All-stop). Considering that the Wikipedia corpus is almost 160 times bigger than the News corpus, it is unsurprising that the word embeddings obtained from the former yield considerably better results, consistent across all nine datasets.
5.2 Evaluating Preprocessing Training Corpora for Word Vectors vs. Preprocessing Classification Data
We investigate the difference between applying preprocessing to the training corpora for generating word embeddings (Pre) and applying preprocessing to the classification datasets (Post). As an example, during Pre, we first apply the preprocessing techniques (e.g., all but stemming) to the training corpus (e.g., Wikipedia), then generate word embeddings, then convert a classification dataset (e.g., IMDB) into word embedding representation, and finally classify using LSTM. Conversely, for Post, we first generate word embeddings from a training corpus (e.g., Wikipedia), then apply the preprocessing techniques (e.g., all but stemming) to the classification dataset (e.g., IMDB), which is then converted to word vector representation, and finally classified using LSTM<sup>16</sup>.
The results of this experiment are presented in Table 6, where we observe that incorporating preprocessing into the training corpora before generat
<sup>16</sup>Note: For settings including stemming, the classification data is also stemmed in order to obtain a compatible vocabulary.
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Models	Processing	IMDB	Semeval	Airline	IAC	Onion	Reddit	Alm	ISEAR	SSEC
CBOW	Basic	84.91	56.89	68.11	69.15	71.02	63.58	45.22	59.73	55.84
All	88.41	60.25	71.39	71.57	73.61	65.27	48.81	62.48	57.42
All - neg	83.02	56.03	69.28	69.55	70.25	64.18	46.00	60.42	55.93
All - pos	85.69	57.21	71.00	70.08	72.29	64.82	47.53	62.28	56.25
All - punc	84.00	57.36	70.46	70.01	72.02	65.00	47.68	61.84	56.64
All - spell	86.19	58.26	70.98	70.59	72.85	65.00	47.29	61.63	57.00
All - stop	91.10	61.00	73.00	72.31	74.50	68.20	52.39	64.29	58.46
All - stem	88.76	62.19	73.25	72.36	75.69	68.53	50.28	65.33	59.28
Skip-gram	Basic	84.00	55.94	68.36	69.20	71.68	63.74	45.01	59.45	55.62
All	87.00	59.99	71.29	71.25	73.82	65.67	48.51	65.02	57.13
All - neg	84.97	56.11	69.00	70.17	70.04	64.55	46.28	60.54	55.86
All - pos	86.21	57.62	70.25	70.85	73.22	65.47	47.49	63.44	56.00
All - punc	85.00	57.20	70.00	70.77	72.00	65.00	47.10	61.72	56.49
All - spell	85.75	58.49	70.26	70.89	72.63	65.18	47.14	61.25	56.84
All - stop	89.76	61.74	72.19	72.00	75.69	68.29	52.01	64.00	58.14
All - stem	89.66	60.28	73.66	71.98	75.24	68.72	51.39	63.44	59.01
BERT	Basic	90.11	70.82	90.23	71.19	76.30	59.74	57.81	65.70	65.39
All	91.86	71.76	91.73	73.66	78.72	62.60	59.74	67.80	67.49
All - neg	90.33	70.52	91.04	72.00	77.07	61.44	58.14	66.59	66.10
All - pos	91.01	71.20	91.66	73.31	78.45	62.04	59.01	66.25	68.13
All - punc	91.59	71.50	91.60	73.18	78.54	62.27	59.60	67.25	67.27
All - spell	91.78	71.13	91.34	73.02	78.40	62.00	59.44	67.21	67.30
All - stop	94.18	73.81	94.85	75.80	79.10	65.39	60.73	69.33	69.81
All - stem	92.19	71.94	92.03	74.49	77.93	63.74	60.16	68.00	67.05
Table 5: F-score results of evaluating the effect of preprocessing factors using different models on Wikipedia corpus. The overall best results are shown in bold.
Models	Processing	IMDB	Semeval	Airline	IAC	Onion	Reddit	Alm	ISEAR	SSEC
CBOW	Post	87.49	59.33	71.28	69.87	74.20	67.13	47.19	62.00	56.27
Pre	88.76	62.19	73.25	72.36	75.69	68.53	50.28	65.33	59.28
Both	88.10	62.41	73.00	71.86	75.00	70.10	50.39	64.52	58.20
Skip-gram	Post	88.14	60.41	71.85	70.22	75.07	67.00	50.44	62.08	56.00
Pre	89.76	61.74	72.19	72.00	75.69	68.29	52.01	64.00	58.14
Both	89.33	61.25	73.58	71.62	75.48	68.74	51.68	65.29	58.03
BERT	Post	94.58	70.25	92.35	74.69	77.10	63.38	58.40	68.20	67.17
Pre	94.18	73.81	94.85	75.80	79.10	65.39	60.73	69.33	69.81
Both	94.63	72.41	93.00	75.19	78.69	65.17	60.33	69.06	68.43
Table 6: F-score results of evaluating the effect of preprocessing word embeddings training corpus vs. preprocessing evaluation datasets
ing word vectors (Pre) outperforms preprocessing classification datasets (Post) across all nine datasets of the three affective tasks. Interestingly though, preprocessing both the bodies of text (Both) appears to be of little benefit, suggesting the importance of preprocessing training corpora used for obtaining word embeddings.
5.3 Evaluating Proposed Model against State-of-the-art Baselines
While not a primary focus of this paper, in this final experiment we compare the performance of our preprocessed word embeddings against those of six state-of-the-art pretrained word embeddings<sup>17</sup>.
(i) GloVe: Global vectors for word representations (Pennington et al., 2014) were trained on aggregated global word co-occurrences. We use the vectors trained on GloVe6B 6 billion words $^{18}$ , uncased, from Wikipedia and Gigaword. (ii) SSWE: Sentiment Specific Word Embeddings (unified model) $^{19}$  were trained using a corpus of 10 million tweets to encode sentiment information into the continuous representation of words (Tang et al., 2014). (iii) FastText: These pretrained word vectors $^{20}$ , based on sub-word character n-grams were trained on Wikipedia using fastText (Bojanowski et al., 2017), an extension of the word2vec model.
$^{18}$ https://nlp.stanford.edu/projects/glove/
<sup>19</sup> http://ir.hit.edu.cn/tilytang/paper/sswe/embedding
results.zip
$^{20}$ https://github.com/facebookresearch/fastText
These vectors obtained from their original repositories have been used without any modifications.
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Models	IMDB	Semeval	Airline	IAC	Onion	Reddit	Alm	ISEAR	SSEC
GloVe	85.64	70.29	70.21	70.19	71.39	63.57	56.21	65.30	58.40
SSWE	80.45	69.27	78.29	64.85	52.74	50.73	51.00	54.71	52.18
FastText	75.26	68.55	70.69	55.74	58.29	59.37	52.28	25.40	53.20
DeepMoji	69.79	62.10	71.03	65.67	70.90	53.08	46.33	58.20	58.90
EWE	71.28	60.27	67.81	67.43	70.06	55.02	58.33	66.09	58.94
Our best results:
CBOW	91.10	62.19	73.25	72.36	75.69	68.53	52.39	65.33	59.28
Skip-gram	89.76	61.74	73.66	72.00	75.69	68.72	52.01	65.02	59.01
BERT	94.18	73.81	94.85	75.80	79.10	65.39	60.73	69.33	69.81
Table 7: F-score results of comparing against state-of-the-art word embeddings. The best score is highlighted in bold, and the second best result is underlined.
(iv) DeepMoji: These word embeddings $^{21}$  were trained using BiLSTM on 1.2 billion tweets with emojis (Felbo et al., 2017). (v) EWE: Emotion-enriched Word Embeddings $^{22}$  were learned on 200,000 Amazon product reviews corpus using an LSTM model (Agrawal et al., 2018).
From the results in Table 7, we notice that BERT is best on eight out of nine datasets except one sarcasm dataset (Reddit), while word2vec CBOW is the second best on four datasets. Overall, our analysis suggests that preprocessing at word embedding stage (Pre) works well for all the three affective tasks.
5.4 Analyzing the Three Affective Tasks
Figure 2 summarizes the results obtained for all three tasks in terms of (a) absolute F-scores and (b) relative improvement (best preprocessing over Basic preprocessing). The IMDB dataset achieves the highest F-score overall, most likely because it consists of movie reviews which are much longer than the text from other genres. As expected, the binary classification task of sentiment analysis and sarcasm detection achieve comparable results, while the multiclass emotion classification typically has much lower F-scores. The most interesting observation, however, is noticed in Fig. 2(b) where the emotion datasets show the highest relative improvement, indicating that multiclass classification tasks may benefit the most from applying preprocessing at word embedding stage (Pre).
We systematically examined the role of preprocessing training corpora used to induce word representations for affect analysis. While all preprocessing techniques improved performance to a certain ex
Figure 2: Absolute F-scores vs. relative improvement
tent, our analysis suggests that the most noticeable increase is obtained through negation processing (neg). The overall best performance is achieved by applying all the preprocessing techniques, except stopwords removal (All-stop). Interestingly, incorporating preprocessing into word representations appears to be far more beneficial than applying it in a downstream task to classification datasets. Moreover, while all the three affective tasks (sentiment analysis, sarcasm detection and emotion classification) benefit from our proposed preprocessing framework, our analysis reveals that the multiclass emotion classification task benefits the most. Exploring the space of subsets of our preprocessing factors might yield more interesting combinations; we leave this for future work.
We thank the anonymous reviewers for their insightful comments. This work is funded by Natural Sciences and Engineering Research Council of Canada (NSERC) and the Big Data Research, Analytics, and Information Network (BRAIN) Alliance established by the Ontario Research Fund Research Excellence Program (ORF-RE). In particular, we thank Majid Taghdimi from Questrade to provide us with the computing resources and help in the parallelization algorithm. We would also like to thank Dr. Heidar Davoudi for the helpful discussions and insights in this project.
$^{21}$ https://github.com/bfelbo/DeepMoji
$^{22}$ https://www.dropbox.com/s/wr5ovupf7y1282x/ewe_uni.txt
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Ameeta Agrawal and Aijun An. 2012. Unsupervised emotion detection from text using semantic and syntactic relations. In Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-Volume 01, pages 346-353. IEEE Computer Society.
Ameeta Agrawal, Aijun An, and Manos Papagelis. 2018. Learning emotion-enriched word representations. In Proceedings of the 27th International Conference on Computational Linguistics, pages 950-961.
Giulio Angiani, Laura Ferrari, Tomaso Fontanini, Paolo Fornacciari, Eleonora Iotti, Federico Magliani, and Stefano Manicardi. 2016. A comparison between preprocessing techniques for sentiment analysis in twitter. In Proceedings of the 2nd International Workshop on Knowledge Discovery on the WEB, KDWeb.
Nastaran Babanejad, Ameeta Agrawal, Heidar Davoudi, Aijun An, and Manos Papagelis. 2019. Leveraging emotion features in news recommendations. In Proceedings of the 7'th International Workshop on News Recommendation and Analytics (INRA'19) in conjunction with RecSys'19, Copenhagen, Denmark, September 16 - 20, 2019.
Farah Benamara, Baptiste Chardon, Yannick Mathieu, Vladimir Popescu, and Nicholas Asher. 2012. How do negation and modality impact on opinions? In Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, ExProM '12, pages 10-18, Stroudsburg, PA, USA. Association for Computational Linguistics.
Erik Boiy, Pieter Hens, Koen Deschacht, and Marie-Francine Moens. 2007. Automatic sentiment analysis in on-line text. In Proceedings of the 11th International Conference on Electronic Publishing ELPUB2007.
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5:135-146.
Jose Camacho-Collados and Mohammad Taher Pilehvar. 2018. On the role of text preprocessing in neural network architectures: An evaluation study on text categorization and sentiment analysis. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. Association for Computational Linguistics.
Ebba Cecilia and Alm Ovesdotter. 2008. Affect in text and speech. ProQuest, CiteSeer.
Billy Chiu, Gamal Crichton, Anna Korhonen, and Sampo Pyysalo. 2016. How to train good word embeddings for biomedical nlp. In Proceedings of the
15th workshop on biomedical natural language processing, pages 166-174.
Toner Danisman and Adil Alpkocak. 2008. Feeler: Emotion classification of text using vector space model. In Proceedings of the AISB 2008 Symposium on Affective Language in Human and Machine, AISB 2008 Convention Communication, Interaction and Social Intelligence, volume 1, page 53.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
Nemanja Djuric, Jing Zhou, Robin Morris, Mihajlo Grbovic, Vladan Radosavljevic, and Narayan Bhamidipati. 2015. Hate speech detection with comment embeddings. In Proceedings of the 24th International Conference on World Wide Web, WWW '15 Companion, page 29-30, New York, NY, USA. Association for Computing Machinery.
Manaal Faruqui, Jesse Dodge, Sujay K Jauhar, Chris Dyer, Eduard Hovy, and Noah A Smith. 2014. Retrofitting word vectors to semantic lexicons. arXiv preprint arXiv:1411.4166.
Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rahwan, and Sune Lehmann. 2017. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. In Proceedings of the 2017 International Conference on Empirical Methods in Natural Language Processing (EMNLP).
Vachagan Gratian and Marina Haid. 2018. Braint at iest 2018: Fine-tuning multiclass perceptron for implicit emotion classification. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 243-247.
Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, and Richard Socher. 2017. A joint many-task model: Growing a neural network for multiple NLP tasks. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1923-1933, Copenhagen, Denmark. Association for Computational Linguistics.
Daniel Hershcovich, Assaf Toledo, Alon Halfon, and Noam Slonim. 2019. Syntactic interchangeability in word embedding models. arXiv preprint arXiv:1904.00669.
Zhao Jianqiang and Gui Xiaolin. 2017. Comparison research on text pre-processing methods on twitter sentiment analysis. IEEE Access, 5:2870-2879.
Mikhail Khodak, Nikunj Saunshi, and Kiran Vodrahalli. 2017. A large self-annotated corpus for sarcasm. arXiv preprint arXiv:1704.05579.
5808
Yanghoon Kim, Hwanhee Lee, and Kyomin Jung. 2018. AttnConvnet at SemEval-2018 task 1: Attention-based convolutional neural networks for multi-label emotion classification. In Proceedings of The 12th International Workshop on Semantic Evaluation, pages 141-145, New Orleans, Louisiana. Association for Computational Linguistics.
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Omer Levy and Yoav Goldberg. 2014. Dependency-based word embeddings. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, volume 2, pages 302-308.
Omer Levy, Yoav Goldberg, and Ido Dagan. 2015. Improving distributional similarity with lessons learned from word embeddings. Transactions of the Association for Computational Linguistics, 3:211-225.
Pierre Lison and Andrey Kutuzov. 2017. Redefining context windows for word embedding models: An experimental study. In Proceedings of the 21st Nordic Conference on Computational Linguistics (NoDaLiDa), pages 284-288, Gothenburg, Sweden. Association for Computational Linguistics.
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011. Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 142-150, Portland, Oregon, USA. Association for Computational Linguistics.
Oren Melamud, David McClosky, Siddharth Patwardhan, and Mohit Bansal. 2016. The role of context types and dimensionality in learning word embeddings. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1030-1040, San Diego, California. Association for Computational Linguistics.
Tomas Mikolov, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. CoRR, abs/1301.3781.
George A. Miller. 1995. Wordnet: A lexical database for english. Association for Computing Machinery, Commun. ACM, 38(11):39-41.
Rishabh Misra and Prahal Arora. 2019. Sarcasm detection using hybrid neural network. arXiv preprint arXiv:1908.07414.
Saif M Mohammad, Parinaz Sobhani, and Svetlana Kiritchenko. 2017. Stance and sentiment in tweets. ACM Transactions on Internet Technology (TOIT), 17(3):26.
Hala Mulki, Chedi Bechikh Ali, Hatem Haddad, and Ismail Babaoglu. 2018. Tw-star at semeval-2018 task 1: Preprocessing impact on multi-label emotion classification. In Proceedings of The 12th International Workshop on Semantic Evaluation, pages 167-171.
Preslav Nakov, Alan Ritter, Sara Rosenthal, Veselin Stoyanov, and Fabrizio Sebastiani. 2016. SemEval 2016 task 4: Sentiment analysis in Twitter. In Proceedings of the 10th International Workshop on Semantic Evaluation, SemEval '16, San Diego, California. Association for Computational Linguistics.
Shereen Oraby, Vrindavan Harrison, Lena Reed, Ernesto Hernandez, Ellen Riloff, and Marilyn Walker. 2016. Creating and characterizing a diverse corpus of sarcasm in dialogue. In Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 31-41, Los Angeles. Association for Computational Linguistics.
Chaitali G. Patil and Sandip Patil. 2013. Use of porter stemming algorithm andsvm for emotion extraction from news headlines. In International Journal of Electronics, Communication and Soft Computing Science and Engineering.
Samuel Pecar, Michal Farkas, Marian Simko, Peter Lacko, and Maria Bielikova. 2018. Nl-fiit at iest-2018: Emotion recognition utilizing neural networks and multi-level preprocessing. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 217-223.
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532-1543.
S Lovelyn Rose, R Venkatesan, Girish Pasupathy, and P Swaradh. 2018. A lexicon-based term weighting scheme for emotion identification of tweets. International Journal of Data Analysis Techniques and Strategies, 10(4):369-380.
Hassan Saif, Miriam Fernandez, Yulan He, and Harith Alani. 2014. On stopwords, filtering and data sparsity for sentiment analysis of twitter. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 810-817, Reykjavik, Iceland. European Language Resources Association (ELRA).
Hendrik Schuff, Jeremy Barnes, Julian Mohme, Sebastian Padó, and Roman Klinger. 2017. Annotation, modelling and analysis of fine-grained emotions on a stance and sentiment detection corpus. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 13-23.
Dibyendu Seal, Uttam K Roy, and Rohini Basak. 2020. Sentence-level emotion detection from text based
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