Purpose: Rapid developments in social networks and their usage in everyday life have caused an explosion in the amount of short electronic documents. Thus, the need to classify this type of document based on their content has a significant implication in many applications. The need to classify these documents in relevant classes according to their text contents should be interested in many practical reasons. Short-text classification is an essential step in many applications, such as spam filtering, sentiment analysis, Twitter personalization, customer review and many other applications related to social networks. Reviews on short text and its application are limited. Thus, this paper aims to discuss the characteristics of short text, its challenges and difficulties in classification. The paper attempt to introduce all stages in principle classification, the technique used in each stage and the possible development trend in each stage.Design/methodology/approach: The paper as a review of the main aspect of short-text classification. The paper is structured based on the classification task stage.Findings: This paper discusses related issues and approaches to these problems. Further research could be conducted to address the challenges in short texts and avoid poor accuracy in classification. Problems in low performance can be solved by using optimized solutions, such as genetic algorithms that are powerful in enhancing the quality of selected features. Soft computing solution has a fuzzy logic that makes short-text problems a promising area of research.Originality/value: Using a powerful short-text classification method significantly affects many applications in terms of efficiency enhancement. Current solutions still have low performance, implying the need for improvement. This paper discusses related issues and approaches to these problems.

Cross-modal retrieval, which aims to search for semantically relevant data across different modalities, has received increasing attention in recent years. Deep learning, with its ability to extract high-level representations from multimodal data, has become a popular approach for cross-modal retrieval. In this paper, we present a comprehensive survey of deep learning techniques for cross-modal retrieval including 35 papers published in recent years. The review is organized into four main sections, covering traditional subspace learning methods, deep learning, and machine learning-based approaches, techniques based on large multi-modal models, and an analysis of datasets used in the field of cross-modal retrieval. We compare and analyze the performance of different deep learning methods on benchmark datasets, the result shows that although a large number of innovative methods have been proposed, there are still some problems that need to be solved, such as multi-modal feature alignment, multi-modal feature fusion, and subspace learning, as well as specialized datasets.

The extracted product aspects (like “battery life”, “zoom”) from online customer reviews are dissimilar in their significances, some of these aspects have a great influence on the potential customer's decision likewise on the businesses' strategies for product enhancements. Supporting the probable customers with a list of the most representative product aspects will assist their purchasing decision and facilitate the comparative process among the presented products. For the firms, identifying critical product aspects creates a new perspective of product manufacturing and marketing strategies to be competitive and innovative. However the manual identification of the most representative product aspects from the huge amounts of the extracted product aspects in online reviews is a tedious and time-consuming task. Thus, ranking the extracted aspects becomes a necessity to identify the important product aspects mentioned in the customer reviews. The purpose of this study is to formulate the product aspect ranking problem as a decision making process using Multi-Criteria Decision Making (MCDM). In response, a comparative analysis between two different MCDM ranking approaches, namely; TOPSIS and VIKOR has been conducted to investigate their performances in prioritizing the most important product aspects in customer reviews. The experimental results on different product reviews demonstrate the effectiveness of these two methods in prioritizing the genuine product aspects in customer feedback.

Travel blogs are a significant source for modeling human travelling behavior and characterizing tourist destinations owing to the presence of rich geospatial and thematic content. However, the bulk of unstructured text requires extensive processing for an efficient transformation of data to knowledge. Existing works have studied tourist places, but results lack a coherent outline and visualization of the semantic knowledge associated with tourist attractions. Hence, this work proposes place semantics extraction based on a fusion of content analysis and natural language processing (NLP) techniques. A weighted-sum equation model is then employed to construct a points of interest graph (POI graph) that integrates extracted semantics with conventional frequency-based weighting of tourist spots and routes. The framework offers determination and visualization of massive blog text in a comprehensible manner to facilitate individuals in travel decision-making as well as tourism managers to devise effective destination planning and management strategies.