EmotionFinder: Detecting Emotion From Blogs and Textual Documents (original) (raw)
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IAEME PUBLICATION, 2018
Interaction of human with computer is very interesting and most famous area of research these days because the word is getting modern and digitize. This needs the digital systems to imitate the human behaviour correctly. Emotion is a particular part of human behaviour which plays an important role while interacting with computer, the computer interfaces need to detect the emotion of the users in order to build a truly intelligent behaviour. Every day, massive amount of textual data is gathered into internet such as blogs, social media etc. With the rapid growth of web application, most of documents are available on web in the form of text. So, detecting affects from text is a vital issue. Hence, attitude detection from text is important in many areas such as decision making, human computer interaction etc. Work done in this field is very less as compare to other fields. Therefore, it broadens our scope in the field of attitude detection. In this paper, we propose a hybrid model that incorporates natural language processing technique, including keyword-based and machine learning-based emotion classification from textual data at sentence level. Using proposed algorithm, one can calculate the affect vector of sentence by affect vector of word. Then based on affect vector categorize the sentence into appropriate affect class.
Emotion Detection From Text Documents
International Journal of Data Mining & Knowledge Management Process, 2014
Emotion Detection is one of the most emerging issues in human computer interaction. A sufficient amount of work has been done by researchers to detect emotions from facial and audio information whereas recognizing emotions from textual data is still a fresh and hot research area. This paper presented a knowledge based survey on emotion detection based on textual data and the methods used for this purpose. At the next step paper also proposed a new architecture for recognizing emotions from text document. Proposed architecture is composed of two main parts, emotion ontology and emotion detector algorithm. Proposed emotion detector system takes a text document and the emotion ontology as inputs and produces one of the six emotion classes (i.e. love, joy, anger, sadness, fear and surprise) as the output.
A Novel Approach for Detecting Emotion in Text
Indian Journal of Science and Technology, 2016
Objectives: In this paper, we present an experiment, which concerned with detection of emotion class at sentence level. Methods: Approach is based upon combination of both machine leaning and key word based approach. There is a large annotated data set which manually classified a sentence beyond six basic emotions: love, joy, anger, sadness, fear, surprise. Findings: Using annotated data set define an emotion vector of key word in input sentence. Novelty: Using an algorithm calculate the emotion vector of sentence by emotion vector of word. Then on the basis of emotion vector categorized the sentence into appropriate emotion class. Results are shown and found good in comparison to individual approach.
A Hybrid Model for Emotion Detection from Text
International Journal of Information Retrieval Research, 2017
Emotions can be judged by a combination of cues such as speech facial expressions and actions. Emotions are also articulated by text. This paper shows a new hybrid model for detecting emotion from text which depends on ontology with keywords semantic similarity. The text labelled with one of the six basic Ekman emotion categories. The main idea is to extract ontology from input sentences and match it with the ontology base which created from simple ontologies and the emotion of each ontology. The ontology extracted from the input sentence by using a triplet (subject, predicate, and object) extraction algorithm, then the ontology matching process is applied with the ontology base. After that the emotion of the input sentence is the emotion of the ontology which it matches with the highest score of matching. If the extracted ontology doesn't match with any ontology from the ontology base, then the keyword semantic similarity approach used. The suggested approach depends on the mea...
Emotion can be expressed in many ways that can be seen such as facial expression and gestures, speech and by written text. Emotion Detection in text documents is essentially a content – based classification problem involving concepts from the domains of Natural Language Processing as well as Machine Learning. In this paper emotion recognition based on textual data and the techniques used in emotion detection are discussed.
Emotion detection using keywords spotting and semantic network IEEE ICOCI 2006
2006 International Conference on Computing & Informatics, 2006
Emotion can be expressed in ways that can be seen such as facial expression and gestures. Emotion can also be heard by detecting prosody features and other vocal characteristics. However in this research, we are interested to detect emotions from textual information. Our main objective is to predict the emotions from textual material such as e-mails that is related to relationship. We work on emotion detection by using keywords spotting method and semantic network method. We examined both the methods and achieved a better result with the semantic network model.
Approaches towards Emotion Extraction from TEXT
Ijca Proceedings on National Conference on Innovative Paradigms in Engineering Technology 2013, 2013
With the growth of internet community, many different textbased documents are produced. This paper presents an overview of the emerging field of emotion detection from text and describes the current generation of detection methods of emotions from the text. Emotion recognition in text is just one of the several dimensions of the task of making the computers make sense of emotions. In this study the main research focus will be on suggestions for designing more efficient and adaptive Natural Language Processing System for the detection of various emotions (sentiment analysis) on the basis of study of important recent techniques.
Ontology-Based Textual Emotion Detection
International Journal of Advanced Computer Science and Applications, 2015
Emotion Detection from text is a very important area of natural language processing. This paper shows a new method for emotion detection from text which depends on ontology. This method is depending on ontology extraction from the input sentence by using a triplet extraction algorithm by the OpenNLP parser, then make an ontology matching with the ontology base that we created by similarity and word sense disambiguation. This ontology base consists of ontologies and the emotion label related to each one. We choose the emotion label of the sentence with the highest score of matching. If the extracted ontology doesn't match any ontology from the ontology base we use the keyword-based approach. This method doesn't depend only on keywords like previous approaches; it depends on the meaning of sentence words and the syntax and semantic analysis of the context.
A Review on Text Based Emotion Recognition System
With development of Internet and Natural Language processing, use of regional languages is also grown for communication. Sentiment Analysis is natural language processing task that mine information from various text forms such as blogs, reviews and classify them on basis of polarity. Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions and opinions of the people towards a particular topic from a structured, semi-structured or unstructured textual data. In this paper, we try to focus our task of sentiment analysis on text data. We examine the sentiment expression to classify the polarity of the text review on a scale of negative to positive and perform feature extraction and ranking and use these features to train our classifier to classify the text data into its correct label.
Emotion detection and sentiment analysis of text
ELSEVIER SSRN SERIES , 2021
The identification of emotions and thoughts in text was an interesting topic of machine learning in natural languages. With some phrases about all details, emotions and feelings show themselves. Many citizens use foreign languages worldwide and many documents are written in English. Some people do not publish the text in (point) form precisely. In comparison to other technicians, we research emotions in monitor ing using text with or without punctuations, so we see how an emotional management device can be designed with certain beneficial approaches. By developing in a particular way, we benefit from tracking and the possibility of identifying the feelings as outcomes more accurately. In this paper we used different methods for identifying the emotions. Naïve bayes classifier, linear SVM, Logistic regression and random forest are used but best accuracy is achieve d by random forest. The challenge that we have solved in this paper is that in this master learning algorithm we recognize feelings or feelings not shared directly on posts, blogs and social networking pages.