Modern WordNet: An Affective Extension of WordNet

Kumar, Dikshit; Kumar, Agam; Singh, Man; Patel, Archana; Jain, Sarika

doi:10.1007/978-3-030-41862-5_51

Cited by 14 publications

(18 citation statements)

References 4 publications

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“…The last set of indicators includes the emotional connotation of the selected narratives. We collect the word count of emotions belonging to two dictionaries, namely the Regressive Imagery dictionary of Martindale (1987) and the WordNet Affect of Strapparava and Valitutti (2004). From the first dictionary we consider only its anxiety dimension, while from the second one we select the following dimensions: anger, contempt, disgust, fear, happiness, sadness and surprise.…”

Section: Discussionmentioning

confidence: 99%

Testing Big Data in a Big Crisis: Nowcasting under COVID-19

et al. 2022

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Section: Discussionmentioning

confidence: 99%

Testing Big Data in a Big Crisis: Nowcasting under COVID-19

et al. 2022

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“…Sentiment lexicons play a vital role in sentiment words detection. In literature, various sentiment lexicons such as SentiWordNet [12], WordNet-Affect [7], SenticNet5 [11], NRC Hashtag Sentiment lexicon [10], MPQA Subjectivity Lexicon [9], SentiSense [8], GI [6], SO-CAL [4], OL [13], AFFIN [5] with different sizes have been built. Many existing sentiment lexicons have limited words to accurately determine the sentiment orientation of a domainspecific sentiment word.…”

Section: B Wide Coverage Sentiment Lexiconsmentioning

confidence: 99%

“…Various sentiment lexicons with prior polarity have been developed for text sentiment classification in the literature. Some of the most well-known and widely used general sentiment lexicons for sen-timent analysis are AFFIN [5], OL [4], SO-CAL [6], WordNet-Affect [7], GI [6], SentiSense [8], MPQA Subjectivity Lexicon [9], NRC Hashtag Sentiment Lexicon [10], SenticNet5 [11], and SentiWordNet [12]. Hu et al [13] have developed a manually compiled sentiment lexicon for sentiment analysis.…”

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confidence: 99%

Sentiment and Context-Aware Hybrid DNN With Attention for Text Sentiment Classification

et al. 2023

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A massive volume of unstructured data in the form of comments, opinions, and other sorts of data is generated in real-time with the growth of web 2.0. Due to the unstructured nature of the data, building an accurate predictive model for sentiment analysis remains a challenging task. While various DNN architectures have been applied to sentiment analysis with encouraging results, they suffer from high dimensional feature space and consider various features equally. State-of-the-art methods cannot properly leverage semantic and sentiment knowledge to extract meaningful relevant contextual sentiment features.This paper proposes a sentiment and context-aware hybrid DNN model with an attention mechanism that {intelligently learns and highlights salient features of relevant sentiment context in the text. We first use integrated wide coverage sentiment lexicons to identify text sentiment features then leverage bidirectional encoder representation from transformers to produce sentiment-enhanced word embeddings for text semantic extraction. After that, the proposed approach adapts the BiLSTM to capture both word order/contextual text semantic information and the long-dependency relation in the word sequence. Our model also employs an attention mechanism to assign weight to features and give greater significance to salient features in the word sequence. Finally, CNN is utilized to reduce the dimensionality of feature space and extract the local key features for sentiment analysis. The effectiveness of the proposed model is evaluated on real-world benchmark datasets demonstrating that the proposed model significantly improves the accuracy of existing text sentiment classification.

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“…It is very good because by improving slang words in training data and testing data in sentiment analysis, the calculation process becomes better and more valid [25]. Wordnet is a language dictionary to find out the similarities, synonyms and antonyms of a word [26]. Wordnet can be applied in natural language processing (NLP) to search for synonyms so that sentiment analysis can identify synonyms and opposites in the dataset.…”

Section: Related Workmentioning

confidence: 99%

Effects of using wordnet and spelling checker on classification methods in sentiment analysis for datasets using Bahasa

Andika

Suharjito

2022

IJEECS

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<span>Sentiment analysis was a system for recognizing and extracting opinions in documents. There were two weaknesses in sentiment analysis. The first weakness was preprocessing in sentiment analysis can’t recognize slang words so that important words that should have been recognized became unrecognizable. The Second was the feature extraction process in sentiment analysis only recognized words based on the form of the word but can’t recognize the similar word. In this paper, we proposed spelling checker and wordnet to fix these weaknesses. We also used k-nearest neighbor (KNN), Naïve Bayes, and decision tree as methods for check classify the text. The purpose of this research was first to know the effects of used Wordnet and spelling checkers in sentiment analysis and second was to improve the data processing process in the existing sentiment analysis. The dataset that we used in the research was a list of tweets in Bahasa. The results showed wordnet and spelling checker make a decrease in the valued of false positives, false negatives, and true negatives in the calculation of the confusion matrix. It can increase the accuracy of the K-NN from 43% to 72%, Naïve Bayes from 41% to 71% and decision tree from 47% to 75%.</span>

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Modern WordNet: An Affective Extension of WordNet

Cited by 14 publications

References 4 publications

Testing Big Data in a Big Crisis: Nowcasting under COVID-19

Testing Big Data in a Big Crisis: Nowcasting under COVID-19

Sentiment and Context-Aware Hybrid DNN With Attention for Text Sentiment Classification

Effects of using wordnet and spelling checker on classification methods in sentiment analysis for datasets using Bahasa

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