Comparing ELM with SVM in the Field of Sentiment Classification of Social Media Text Data

Now-a-days, it is very common that the customers share their thoughts about any product, brand and their experience in social media. The analysts collect these reviews and process it, to extract meaningful information about the product. The beauty of social media is, it’s involved in all the domains. So the analysts got reviews from different social media and platforms for almost all kind of thing. The Sentiment Analysis is applied to predict outcomes for getting useful information, for ex.; like predict the blockbuster for a movie, rating for any new launches and many more. This type of prediction is really helpful for the customer to buy any goods or take any services in this competitive world. This paper is focused on e-commerce website reviews which are normally in text form with some special characters and some symbols (emojis). Each word in this text set got some meaning in terms of context, emotion and prior experience. These characteristics contribute to some of the features of text data for prediction. The objective of this paper is to compile existing research works on text analysis and emotion based analysis. The open issues and challenges of document based sentiment analysis are also discussed. The paper concluded with proposing a new approach of multi class classification. Ternary classification for classes positive, negative and neutral is suggested primarily for product based text and emoji reviews on Twitter social media.

Download Full-text

Concept of TF-IDF, Common Bag of Word and Word Embedding for Effective Sentiment Classification

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f4582.049620 ◽

2020 ◽

Vol 9 (4) ◽

pp. 2198-2201

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Sentiment Classification ◽

Word Embedding ◽

Text Representation ◽

Human Beings ◽

Text Data

Sentiment Classification is one of the well-known and most popular domain of machine learning and natural language processing. An algorithm is developed to understand the opinion of an entity similar to human beings. This research fining article presents a similar to the mention above. Concept of natural language processing is considered for text representation. Later novel word embedding model is proposed for effective classification of the data. Tf-IDF and Common BoW representation models were considered for representation of text data. Importance of these models are discussed in the respective sections. The proposed is testing using IMDB datasets. 50% training and 50% testing with three random shuffling of the datasets are used for evaluation of the model.

Download Full-text

Sentiment Classification of Hotel Reviews in Social Media with Decision Tree Learning

International Journal of Computer Applications ◽

10.5120/ijca2017912806 ◽

2017 ◽

Vol 158 (5) ◽

pp. 1-7

Author(s):

Stanimira Yordanova ◽

Dorina Kabakchieva

Keyword(s):

Social Media ◽

Decision Tree ◽

Sentiment Classification ◽

Decision Tree Learning

Download Full-text

CyberCan: A New Dictionary for Cantonese Social Media Text Segmentation

10.31235/osf.io/tyjr7 ◽

2021 ◽

Author(s):

Fei Shen ◽

Wenting Yu ◽

Chen Min ◽

Qianying Ye ◽

Chuanli Xia ◽

...

Keyword(s):

Social Media ◽

Text Mining ◽

Word Segmentation ◽

Unstructured Data ◽

Text Segmentation ◽

Chinese Word ◽

Chinese Word Segmentation ◽

Text Data ◽

Social Media Text

Text mining has been a dominant approach to extracting useful information from massive unstructured data online. But existing tools for Chinese word segmentation are not ideal for processing social media text data in Cantonese. This project developed CyberCan (https://github.com/shenfei1010/CyberCan), a lexicon of contemporary Cantonese based on more than 100 million pieces of internet texts. We compared the performance of CyberCan with existing Mandarin and Cantonese lexicons in terms of their word segmentation performance. Findings suggest that CyberCan outperforms all existing lexicons by a considerable margin.

Download Full-text

Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining

European Journal of Personality ◽

10.1002/per.2290 ◽

2020 ◽

Vol 34 (5) ◽

pp. 826-844 ◽

Cited By ~ 1

Author(s):

Louis Tay ◽

Sang Eun Woo ◽

Louis Hickman ◽

Rachel M. Saef

Keyword(s):

Machine Learning ◽

Social Media ◽

Text Mining ◽

Personality Assessment ◽

Ground Truth ◽

Psychometric Validation ◽

Learning Approaches ◽

Text Data ◽

Personality Psychology ◽

Social Media Text

In the age of big data, substantial research is now moving toward using digital footprints like social media text data to assess personality. Nevertheless, there are concerns and questions regarding the psychometric and validity evidence of such approaches. We seek to address this issue by focusing on social media text data and (i) conducting a review of psychometric validation efforts in social media text mining (SMTM) for personality assessment and discussing additional work that needs to be done; (ii) considering additional validity issues from the standpoint of reference (i.e. ‘ground truth’) and causality (i.e. how personality determines variations in scores derived from SMTM); and (iii) discussing the unique issues of generalizability when validating SMTM for personality assessment across different social media platforms and populations. In doing so, we explicate the key validity and validation issues that need to be considered as a field to advance SMTM for personality assessment, and, more generally, machine learning personality assessment methods. © 2020 European Association of Personality Psychology

Download Full-text