Exploring Diverse Features for Sentiment Quantification Using Machine Learning Algorithms

Abstract

In the era of web 2.0, online forums, blogs and Twitter are becoming primary sources for sharing views, opinions and comments about different topics. Classifying these views, opinions and comments is known as sentiment analysis which is an active research area. Sentiment analysis has vast applications in different fields of life, such as marketing, e-commerce and business. Under the umbrella of sentiment analysis, sentiment quantification that deals with estimating relative frequency of class of interest is being investigated by researchers nowadays. In sentiment quantification, exploring effect of new features and comparison of diverse types of classifiers to assess their effectiveness needs further investigation. In this paper, we explore diverse feature sets and classifiers for sentiment quantification. In addition, empirical performance analysis of conventional machine learning techniques, ensemble-based methods and state-ofthe-art deep learning algorithms on basis of features set, is performed. The computed results show that the diverse features sets affect the performance of classifiers in sentiment quantification. The results also confirm that the deep learning techniques perform better than the conventional machine learning algorithms.

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

“…Term Frequency Inverse Document Frequency (TF-IDF): TF-IDF [2,12,13,18,43] calculates how important a word is to a document in a group of documents which is done by multiplying two metrics. It checks the appearance of words in a document and the document frequency in inverse of the word across a set of documents.…”

“…N-Gram : Type of probabilistic language model [16,43,46] for predicting the next item in such a sequence in the form of a (n 1) order Markov model. An N-gram means a sequence of N words.…”

“…GloVe: GloVe [11,14,[41][42][43]53], a unsupervised learning algorithm by mapping words into a meaningful space for obtaining words vector representations where the semantic similarity is related to the distance between words.…”

“…Feature extraction technique Manoj Sethi [2], Yuwen Lyu [12], Nafiz Al Asad [13], Subhan Tariq [18], Kashif Ayyab [43], Hay Mar Su Aung [74], Alex M. G. Almeida [77], Govin Gaikwad [79], Tianyi Wang [83], Masum Billah [94], Rinki Chatterjee [95] Term Frequency Inverse Document Frequency (TF-IDF) Ganzalo A. Ruz [4], Li Chen Cheng [11], Guozheng Rao [19], Rıza Velioglu [97], Md. Mokhlesur Rahmana [100] Bag of Words (BOW) Ganzalo A. Ruz [4], Li Chen Cheng [11], Md.…”

“…Mokhlesur Rahmana [100] Bag of Words (BOW) Ganzalo A. Ruz [4], Li Chen Cheng [11], Md. Mokhlesur Rahmana [100] Term Document Matrix Micheal T. Tadasse [16], Kashif Ayyab [43], Maruo Dang [46], Jayakrishnan [82] N Gram Kashif Ayyab [43] TD-IDF + N Grams Fazeel Abid [11], Hudan Studiawan [41], Mohd Usama [42], Kashif Ayyab [43], Wei Lun Lin [53] GloVe Micheal T. Tadasse [16], Md. Rafiqul Islam [24], Xingwei Yang [5], Dilesh Tama [7] LIWC Fazeel Abid [11], Mehmet Umut Salur [38], Ali Feizollah [44], Wei Lun Lin [53], Irum Sindhu [56], Da Li [65], Hay Mar Su Aung [74], Shan Huang [90] word2vec Badr Ait Hamou [15], Mehmet Umut Salur [38] FastText Ali Feizollah [44] word2seq L. Ming Dang [49] stock2vec Wei Lun Lin [53] Static Word Embedding which learns more accurate in its predictions [2].…”