# Short Text Classification with Tolerance-Based Soft Computing Method
## 1 Introduction  
Text classification, also known as text categorization, is a classical problem in Natural Language Processing (NLP), which aims to assign labels to textual units such as documents, sentences, paragraphs, and queries. It has a wide range of applications including sentiment analysis, news categorization, question answering, user intent classification, spam detection, and content moderation, to name a few [1]. Popular NLP research areas where the user opinions are analyzed to detect sentiment polarity include opinion mining and sentiment analysis [2]. Polarity determination has been performed for product reviews, forums, blogs, news articles, and micro-blogs. The field of sentiment analysis is greatly aided by a rich and large source of information from platforms such as Twitter. The tasks of Twitter sentiment analysis include sentiment polarity detection, Twitter opinion retrieval, tracking sentiments over time [3], irony detection, and emotion detection [4; 5; 6; 7; 8]. Due to the word limit of 280 characters, micro-blogs do not contain complete sentences. Moreover, micro-blogs often containabbreviations and noisy texts. Therefore, it needs standard pre-processing techniques such Parts-of-Speech (POS) tagging, removing of URLs, hashtags, usernames, stopwords, stemming, and spelling correction to be applied to tweets due to the nature of messages posted by users. Twitter sentiment classification identifies different polarities (e.g., positive, negative, or neutral). Classification is based on textual features which can take different forms such as (i) syntactic (e.g., n-grams, term frequencies, dependency trees), (ii) semantic (e.g., opinion and sentiment words), and usually with the aid of lexicons, (iii) stylistic (e.g., emoticons) and (iv) Twitter-specific features (e.g., hashtags and retweets). The main challenges encountered with tweets are the length of the text (max. of 280 characters) and incorrect or improper use of language. The following machine learning approaches are popular with text classification: supervised [9], semi-supervised [10] and unsupervised [11; 12]. Well-known sentiment lexicons such VADER (Valence Aware Dictionary for Sentiment Reasoning) [13] were developed as an improvement over NLTK and Textblob tools. In [14; 15; 16; 17], deep convolutional and recurrent neural networks were used in sentiment analysis. In this paper, we present a soft computing technique-based algorithm (TSC) to classify sentiment polarities of tweets and news categories from text. The TSC algorithm is a novel supervised learning method based on tolerance near sets. Near sets theory [18; 19] is a more recent soft computing methodology inspired by rough sets [20] where instead of set approximation operators used by rough sets to induce tolerance classes, the tolerance classes are directly induced from the feature vectors using a tolerance level parameter \(\varepsilon\) and a distance function. The tolerance forms of rough sets have been shown to be more effective in text categorization applications [21] where overlapping classes are induced by a _tolerance relation_. The tolerance near set-based classification algorithm was first introduced in [22]. Other applications of near sets in audio signal classification, music genre classification, and community detection in social network can be found in [23]. A theoretical treatment of the relationship between near and rough sets can be found in [24]. In this paper, we explore the effect of different vector-generation methods, tolerance class sizes, balanced and imbalanced datasets as well as a number of sentiment classes on the TSC algorithm. This paper is an extension of our previous work [25]. The extensions include experimentation on three additional text datasets, new formal definition on text-based tolerance relation, comparative work using TF-IDF vectors, additional metrics besides weighted F1, and a statistical test to observe the difference in classifiers.