Text Classification: A Review, Empirical, and Experimental Evaluation

The explosive and widespread growth of data necessitates the use of text classification to extract crucial information from vast amounts of data. Consequently, there has been a surge of research in both classical and deep learning text classification methods. Despite the numerous methods proposed in the literature, there is still a pressing need for a comprehensive and up-to-date survey. Existing survey papers categorize algorithms for text classification into broad classes, which can lead to the misclassification of unrelated algorithms and incorrect assessments of their qualities and behaviors using the same metrics. To address these limitations, our paper introduces a novel methodological taxonomy that classifies algorithms hierarchically into fine-grained classes and specific techniques. The taxonomy includes methodology categories, methodology techniques, and methodology sub-techniques. Our study is the first survey to utilize this methodological taxonomy for classifying algorithms for text classification. Furthermore, our study also conducts empirical evaluation and experimental comparisons and rankings of different algorithms that employ the same specific sub-technique, different sub-techniques within the same technique, different techniques within the same category, and categorie

Semi-supervised sentiment clustering on natural language texts

In this paper, we propose a semi-supervised method to cluster unstructured textual data called semi-supervised sentiment clustering on natural language texts. The aim is to identify clusters homogeneous with respect to the overall sentiment of the texts analyzed. The method combines different techniques and methodologies: Sentiment Analysis, Threshold-based Naïve Bayes classifier, and Network-based Semi-supervised Clustering. It involves different steps. In the first step, the unstructured text is transformed into structured text, and it is categorized into positive or negative classes using a sentiment analysis algorithm. In the second step, the Threshold-based Naïve Bayes classifier is applied to identify the overall sentiment of the texts and to define a specific sentiment value for the topics. In the last step, Network-based Semi-supervised Clustering is applied to partition the instances into disjoint groups. The proposed algorithm is tested on a collection of reviews written by customers on Booking.com. The results have highlighted the capacity of the proposed algorithm to identify clusters that are distinct, non-overlapped, and homogeneous with respect to the overall sentiment. Results are also easily interpretable thanks to the network representation of the instances that helps to understand the relationship between them

Distributed Representations of Sentences and Documents

Many machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts, one of the most common fixed-length features is bag-of-words. Despite their popularity, bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. For example, "powerful," "strong" and "Paris" are equally distant. In this paper, we propose Paragraph Vector, an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents. Our algorithm represents each document by a dense vector which is trained to predict words in the document. Its construction gives our algorithm the potential to overcome the weaknesses of bag-of-words models. Empirical results show that Paragraph Vectors outperform bag-of-words models as well as other techniques for text representations. Finally, we achieve new state-of-the-art results on several text classification and sentiment analysis tasks