Emotion Detection and Sentiment Analysis in Text Corpus: A Differential Study with Informal and Formal Writing Styles

Kaur, Jasleen; Saini, Jatinderkumar R.

doi:10.5120/17712-8078

Cited by 31 publications

(17 citation statements)

References 21 publications

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“…Preprocessing is the process of cleaning the data from unwanted elements and has been applied in specific emotion prediction, e.g. removal of stop words and stemming [4]. Feature selection allows a more accurate analysis of the sentiments and detailed summarization of the results.…”

Section: Introductionmentioning

confidence: 99%

“…The most common feature is unigrams which is found in many research works, e.g. [4]. A variety of machine learning techniques have been used for polarity and emotions prediction from text.…”

Section: Introductionmentioning

confidence: 99%

“…A variety of machine learning techniques have been used for polarity and emotions prediction from text. In our experiments we used classifiers previously shown to work well [6,4]: Naive Bayes (NB), Multinomial Naive Bayes (MNB), Complement Naive Bayes (CNB), Support Vector Machines (SVM), Maximum Entropy (ME), Sequential Minimal Optimization (SMO), and Random Forest (RF). The models are evaluated using accuracy, precision, recall, F-score, Area under curve (AUC), kappa statistic and error rate.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Students’ Emotions Using Machine Learning Techniques

Altrabsheh

Cocea

Fallahkhair

2015

Lecture Notes in Computer Science

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Abstract. Detecting students' real-time emotions has numerous benefits, such as helping lecturers understand their students' learning behaviour and to address problems like confusion and boredom, which undermine students' engagement. One way to detect students' emotions is through their feedback about a lecture. Detecting students' emotions from their feedback, however, is both demanding and time-consuming. For this purpose, we looked at several models that could be used for detecting emotions from students' feedback by training seven different machine learning techniques using real students' feedback. The models with a single emotion performed better than those with multiple emotions. Overall, the best three models were obtained with the CNB classifier for three emotions: amused, bored and excitement.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Students’ Emotions Using Machine Learning Techniques

Altrabsheh

Cocea

Fallahkhair

2015

Lecture Notes in Computer Science

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“…There is a minor difference between Sentiment Analysis and emotion detection. In general, Sentiment Analysis divides text into two binary states (positive/ negative) whereas Emotion Detection uses larger set of emotional states like joy, fear, anger, brief, surprise or disgust for division of text [7]. The customer reviews is provided in the form of text or emotions expressed with the help of emoticons.…”

Section: Text Mining and Emoticon Analysismentioning

confidence: 99%

Emoticon Analysis with Dynamic Text based Opinion Mining

Kadge¹,

Panchbhai²,

Padghawala³

et al. 2016

IJCA

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Social networking is a very popular communication tool where opinions are shared by millions of users thereby generating tremendous data every day. Adjectives in this data represent emotions such as good, amazing etc. for positive nature and poor, bad, inferior for negative nature. Also emoticons such as  for happy feeling and  for sad feeling etc are used which are necessary to be treated along with adjectives to know blogger"s expression. Opinion mining is generally performed by matching words to a dictionary or a corpus that contains static set of words. However if the user uses new words which are not present in the existing dictionary, those words may be neglected. To overcome this shortcoming, this paper proposes a novel algorithm concerned with auto addition of new words at runtime thus making the dictionary dynamic. The algorithm not only gives ratings for adjectives but also for emoticons. The polarity of these newly added words will be defined by considering the polarity of its neighboring words. The analysis shows that novel algorithm gives more accurate ratings than the previous findings. These accurate ratings can help users to choose the products wisely before buying thereby benefiting the vendor as well as users.

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“…Enron has been a very popular dataset for social network analysis (Chapanond et al, 2005;Diesner et al, 2005;Shetty and Adibi, 2005;Oselio et al, 2014;Ahmed and Rossi, 2015) and sentiment and authority analysis (Diesner and Evans, 2015;Liu and Lee, 2015;Miller and Charles, 2016;Mohammad and Yang, 2011). Peterson et al (2011) present an approach to model formality on the ENRON corpus and Kaur et al (2014) compare emotions across formal and informal emails. Jabbari et al (2006) analyze business and personal emails as different classes of data.…”

Section: Related Workmentioning

confidence: 99%

Frustrated, Polite, or Formal: Quantifying Feelings and Tone in Email

Chhaya¹,

Chawla²,

Goyal³

et al. 2018

Proceedings of the Second Workshop on Computational Modeling Of People’s Opinions, Personality, and Emotions in Socia

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Email conversations are the primary mode of communication in enterprises. The email content expresses an individual's needs, requirements and intentions. Affective information in the email text can be used to get an insight into the sender's mood or emotion. We present a novel approach to model human frustration in text. We identify linguistic features that influence human perception of frustration and model it as a supervised learning task. The paper provides a detailed comparison across traditional regression and word distribution-based models. We report a mean-squared error (M SE) of 0.018 against human-annotated frustration for the best performing model. The approach establishes the importance of affect features in frustration prediction for email data. We further evaluate the efficacy of the proposed feature set and model in predicting other tone or affects in text, namely formality and politeness; results demonstrate a comparable performance against the state-of-the-art baselines.

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Emotion Detection and Sentiment Analysis in Text Corpus: A Differential Study with Informal and Formal Writing Styles

Abstract: Text, either online or offline can be presented in two different writing styles: formal and informal writing style.

Cited by 31 publications

References 21 publications

Predicting Students’ Emotions Using Machine Learning Techniques

Predicting Students’ Emotions Using Machine Learning Techniques

Emoticon Analysis with Dynamic Text based Opinion Mining

Frustrated, Polite, or Formal: Quantifying Feelings and Tone in Email

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