Emotion Mining From Text using Machine Learning

Emotions are very meaningful aspects and key factor of human nature and behavior. Emotion Mining is the science of detect, analyze and evaluate human feelings towards different interest, services or any other issues. However, more generally, it refers to determining one's attitude towards a particul

Project Title

Emotion Mining From Text using Machine Learning

Project Area of Specialization

Artificial Intelligence

Project Summary

Emotions are very meaningful aspects and key factor of human nature and behavior. Emotion Mining is the science of detect, analyze and evaluate human feelings towards different interest, services or any other issues. However, more generally, it refers to determining one's attitude towards a particular target or topic. Here, attitude can mean an evaluative judgment, such as positive or negative, or an emotional or effectual attitude such as frustration, happy, joy, anger, sadness, excitement, and so on. Human emotions can be expressed through various media, such as speech, facial expressions, gestures and textual data

Project Objectives

The main problem domain is mining emotions from text at sentence level. The most common way for people to express their opinions, thoughts and communicate with each other is via written text. Text is the main communication mean and the backbone of the web and of social media. Every day, a vast amount of articles and text messages are posted in various sites, blogs, news portals, e-shops, social networks and forums. Mostly the research on the use of social media classifies sentiments into three categories positive, negative and neutral. In this project, the different classifiers use to analyze natural language and recognize the emotional content of text, is presented and its functionality. The analysis of the natural language is conducted at sentence level, so a given document is split in sentences. Many documents and articles may contain various emotional states, even about the same entities. So, systems and approaches that want to have a more fine grained view of the different sentiments expressed in a document regarding entities or the writer's feelings, must deal with sentence level.

Project Implementation Method

We try to implement multiple classification algorithms including naive Bayes, random forest, support vector machine and a maximum entropy learner are trained to recognize sentiments in textual data. And try to improve classifiers performance, evaluation results of emotional status and recognizing emotion presence. The learners are trained using the affective text datasets

Benefits of the Project

Sentiment analysis from text consists of extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. It is often equated to opinion mining, but it should also encompass emotion mining. Opinion mining involves the use of natural language processing and machine learning to determine the attitude of a writer towards a subject. Emotion mining is also using similar technologies but is concerned with detecting and classifying writers emotions toward events or topics. Textual emotion-mining methods have various applications, including gaining information about customer satisfaction, helping in selecting teaching materials in e-learning, recommending products based on users emotions, and even predicting mental-health disorders.

Technical Details of Final Deliverable

Output will be a trained Machine Learning Model.

Final Deliverable of the Project

Software System

Core Industry

Others

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Decent Work and Economic Growth

Required Resources

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