TeamCEN at SemEval-2018 Task 1: Global Vectors Representation in
            Emotion Detection

George, Anon; Ganesh, H B Barathi; M, Anand Kumar; Soman, K P

doi:10.18653/v1/s18-1050

Cited by 15 publications

(8 citation statements)

References 5 publications

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“…Of these classifiers used SVM outperforms well by getting a result of about 80% [16,12]. The model is trained were in the glovec is used [13,15] which creates word vector for text classification task on sentimental analysis were SVM outperforms well with an accuracy of 95% [15]. The dataset uses three languages such as the English, Spanish and Arabic, the dataset is annotated based on emotions such as the joy, fear, angry and sadness.…”

Section: Related Workmentioning

confidence: 99%

“…The dataset uses three languages such as the English, Spanish and Arabic, the dataset is annotated based on emotions such as the joy, fear, angry and sadness. The experimentation are done using Glovec [15] pretrained model which contains 27 billion token and 2 billion tweets. Glovec and the SVD are used for the feature extraction task, and the extracted features are passed to random forest and the SVM for the classification [14].…”

Section: Related Workmentioning

confidence: 99%

“…These analysis are more helpful in industry sectors such as brand monitoring, mobile phones, clothes, cosmetics etc. Mostly online service like swiggy, dominos, amazon, flipkart improve their services by analyzing the customer reviews, when it comes to understanding the psychological behavior of a person pertaining to a situation in which how the person is being affected and how each person solvers the problems also taken in to consideration [3,15]. To understand such psychological behavior and peoples need the tweet from the social media is collected and analyzed according to the emotion contents in the messages.…”

Section: Introductionmentioning

confidence: 99%

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Amrita-CEN-Senti-DB:Twitter Dataset for Sentimental Analysis and Application of Classical Machine Learning and Deep Learning

Ravi¹

2020

Preprint

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Social media is a platform in which tons and tons of text are generated each and every day. The data is so large that cannot be easily understood, so this has paved a path to a new field in the information technology which is natural language processing. In this paper, the text data which is used for the classification is tweets that determines the state of the person according of the sentiments which is positive, negative and neutral. Emotions are the way of expression of the person's feelings which has a high influence on the decision making tasks. Here we have proposed the text representation, Term Frequency Inverse Document Frequency (tfidf), Keras embedding along with the machine learning and deep learning algorithms for the purpose of the classification of the sentiments, out of which Logistics Regression machine learning based methods out performs well when the features is taken in the limited amount as the features increases Support Vector Machine (SVM) which is also one of the machine learning algorithm out performs well making a benchmark accuracy for this dataset as the 75.8%. For the research purpose the dataset has been made publically available.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Amrita-CEN-Senti-DB:Twitter Dataset for Sentimental Analysis and Application of Classical Machine Learning and Deep Learning

Ravi¹

2020

Preprint

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“…In [10] George et al experimented the intensity prediction as a text classification problem that evaluates the distributed representation text using aggregated sum and dimensionality reduction of the glove vectors of the words present in the respective texts (English and Arabic language).…”

Section: Table 1 Corresponding Emotion Reflection In Tweetsmentioning

confidence: 99%

Emotion analysis of Arabic tweets using deep learning approach

Baali

Ghneim

2019

J Big Data

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In our daily life, every one of us face different situations and the outcome of it is developing a feeling about it. Emotion is a strong feeling about human's situation or relation with others [1]. It has a big role in customer decision in many domains including e-commerce, restaurants, movies, interests, and satisfaction with a service or a product. Moreover, it affects our health! Lately, Facebook added some reactions including angry, happiness, love, and surprise to allow users to express their emotions toward a comment, picture or an event. In research, emotional analysis is regarded as a sort of higher, evolved form of sentiment analysis. Sentiment analysis aims to classify texts (posts, sentences, or documents) into negative, positive or neutral. Emotional analysis, on the other hand, is a more elaborated, deeper analysis of users' emotions that tries to inspect the psychology of different Abstract Nowadays, sharing moments on social networks have become something widespread. Sharing ideas, thoughts, and good memories to express our emotions through text without using a lot of words. Twitter, for instance, is a rich source of data that is a target for organizations for which they can use to analyze people's opinions, sentiments and emotions. Emotion analysis normally gives a more profound overview of the feelings of an author. In Arabic Social Media analysis, nearly all projects have focused on analyzing the expressions as positive, negative or neutral. In this paper we intend to categorize the expressions on the basis of emotions, namely happiness, anger, fear, and sadness. Different approaches have been carried out in the area of automatic textual emotion recognition in the case of other languages, but only a limited number were based on deep learning. Thus, we present our approach used to classify emotions in Arabic tweets. Our model implements a deep Convolutional Neural Networks (CNN) trained on top of trained word vectors specifically on our dataset for sentence classification tasks. We compared the results of this approach with three other machine learning algorithms which are SVM, NB and MLP. The architecture of our deep learning approach is an end-to-end network with word, sentence, and document vectorization steps. The deep learning proposed approach was evaluated on the Arabic tweets dataset provided by SemiEval for the EI-oc task, and the results-compared to the traditional machine learning approaches-were excellent.

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“…These tweets is a paving path to textual analysis in the field of Natural language processing. In sentimental analysis "sentiment" in the tweet is considered in first place, mainly sentiment comes under the categorization of happy, sad, angry [9,15].…”

Section: Introductionmentioning

confidence: 99%

Amrita-CEN-Senti-DB:Twitter Dataset for Sentimental Analysis and Application of Classical Machine Learning and Deep Learning

Ravi¹

2020

Preprint

View full text Add to dashboard Cite

Social media is a platform in which tons and tons of text are generated each and every day. The data is so large that cannot be easily understood, so this has paved a path to a new field in the information technology which is natural language processing. In this paper, the text data which is used for the classification is tweets that determines the state of the person according of the sentiments which is positive, negative and neutral. Emotions are the way of expression of the person’s feelings which has a high influence on the decision making tasks. Here we have proposed the text representation, Term Frequency Inverse Document Frequency (tfidf), Keras embedding along with the machine learning and deep learning algorithms for the purpose of the classification of the sentiments, out of which Logistics Regression machine learning based methods out performs well when the features is taken in the limited amount as the features increases Support Vector Machine (SVM) which is also one of the machine learning algorithm out performs well making a benchmark accuracy for this dataset as the 75.8%. For the research purpose the dataset has been made publically available.

show abstract

TeamCEN at SemEval-2018 Task 1: Global Vectors Representation in Emotion Detection

Cited by 15 publications

References 5 publications

Amrita-CEN-Senti-DB:Twitter Dataset for Sentimental Analysis and Application of Classical Machine Learning and Deep Learning

Amrita-CEN-Senti-DB:Twitter Dataset for Sentimental Analysis and Application of Classical Machine Learning and Deep Learning

Emotion analysis of Arabic tweets using deep learning approach

Amrita-CEN-Senti-DB:Twitter Dataset for Sentimental Analysis and Application of Classical Machine Learning and Deep Learning

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