IMPROVING OPINION MINING BY CLASSIFYING FACTS AND OPINIONS IN TWITTER - A DEEP LEARNING APPROACH

The massive social media data presents businesses with an immense opportunity to extract useful insights. However, social media messages typically consist of both facts and opinions, posing a challenge to analytics applications that focus more on either facts or opinions. Distinguishing facts and opinions from social media may significantly improve both, fact seeking applications that aims to capture breaking news, as well as user opinion seeking applications that aims to evaluate users\u27 sentiment towards an event or entity. Despite, the growing need, classifying facts from opinion in social media, has gained minimal attention. In this study we examine the limitation of applying existing, subjectivity detection methods that identifies subjective contents in textual data. In the context of social media, specifically in microblogs like Twitter, the content is dirty with respect to spelling, syntax, extensive usage of emoticons and abbreviation apart from the overall issue of data sparsity. Traditional methods of checking individual words against a predefined lexicon data set, do not often yield required accuracy for this task. Primary objective of this study is to address this limitation and provide an alternative method to improve this classification task and opinion mining in general. The study proposes usmg supplemental information from Twitter metadata and empirically demonstrates the improvement in performance. To ensure rigor and relevance, design science research methodology is adopted for this project. We propose a deep learning algorithm that automatically separates facts from opinions in Twitter messages. Our model combines bag-of-word features with selected manually-engineered features from Twitter metadata in a multipm1 experiment. We leverage an external reference dataset to develop our manually-engineered feature variables and evaluated efficiency against three external baseline tools. The study uses eight different machine learning classifiers to demonstrate the robustness of the manual feature set. Next, we combine these manually-engineered features with features extracted from bag-of-words model in our proposed deep learning model. Our algorithm significantly outperformed multiple popular baselines in the internal evaluation pm1 of the experiment. Next as part of practical usefulness, we illustrated how distinguishing facts and opinions can be useful in a real world business application. We applied our proposed algorithm to an external opinion mining application that tracks emerging customer complaints from social media conversation. We conducted our case study with three large financial institutions using Twitter data for a period of 16 weeks. The study observed considerable improvement in that external application after integrating our algorithm and concludes that it indeed benefit subsequent analytics applications

The Development of a Temporal Information Dictionary for Social Media Analytics

Dictionaries have been used to analyze text even before the emergence of social media and the use of dictionaries for sentiment analysis there. While dictionaries have been used to understand the tonality of text, so far it has not been possible to automatically detect if the tonality refers to the present, past, or future. In this research, we develop a dictionary containing time-indicating words in a wordlist (T-wordlist). To test how the dictionary performs, we apply our T-wordlist on different disaster related social media datasets. Subsequently we will validate the wordlist and results by a manual content analysis. So far, in this research-in-progress, we were able to develop a first dictionary and will also provide some initial insight into the performance of our wordlist

Emotional Tendency Analysis of Twitter Data Streams

The web now seems to be an alive and dynamic arena in which billions of people across the globe connect, share, publish, and engage in a broad range of everyday activities. Using social media, individuals may connect and communicate with each other at any time and from any location. More than 500 million individuals across the globe post their thoughts and opinions on the internet every day. There is a huge amount of information created from a variety of social media platforms in a variety of formats and languages throughout the globe. Individuals define emotions as powerful feelings directed toward something or someone as a result of internal or external events that have a personal meaning. Emotional recognition in text has several applications in human-computer interface and natural language processing (NLP). Emotion classification has previously been studied using bag-of words classifiers or deep learning methods on static Twitter data. For real-time textual emotion identification, the proposed model combines a mix of keyword-based and learning-based models, as well as a real-time Emotional Tendency Analysi

Sentiment Classification using Machine Learning: A Survey

The World Wide Web has brought a new way of expressing the reactions of people about any product, things, and topics, etc. The sentiment Analysis of written textual content on the web is one of the text mining areas used to find out sentiments in a given text. The process of sentiment analysis is a task of detecting, extracting and classifying critiques and sentiments expressed in texts. Twitter is also a medium with the huge amount of information wherein users can view the opinion of other users that labeled into different sentiment classes such as positive, negative, and neutral and are increasingly more developing as a key element in decision making. ?Till now, there are few extraordinary problems predominating in this research community, namely, sentiment classification, feature-based classification and dealing with negations. This paper presents a survey covering the strategies and techniques of sentiment classification and demanding situations appear within the area.