Learning Concept Hierarchy from Short Texts Using Context Coherence

Almars, Abdulqader M.; Li, Xue; Ibrahim, Ibrahim A.; Zhao, Xin

doi:10.1007/978-3-030-02922-7_22

Cited by 4 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To solve the first drawback, Almars et al [35] propose a top-down recursive model called context coherence-based model (CCM). This model analyzes the relations between words to discover a concept hierarchy from short text automatically without a pre-defended hierarchy depth and width.…”

Section: Hierarchical Sentiment Analysis Methodsmentioning

confidence: 99%

A Study of Sentiment Analysis Approaches in Short Text

Ibrahim

Hassaballah

Ali

et al. 2021

Digital Transformation Technology

Self Cite

View full text Add to dashboard Cite

Recently, the remarkable growth of Internet technology, particularly on social media networking sites, enables gathering data for analyzing and gaining insights. It is challenging to analyze such a huge amount of information that causes time-consuming. So, it is necessary to make an intelligent system that automatically analyzes a great amount of data. Sentiment analysis methods appear to analyze sentiments and opinions of people through what they write on social networking sites. Different sentiment analysis approaches have been proposed to understand the sentiments and opinions expressed by the individuals in the text. However, some methods produce an improper result when applied to short text due to text briefness and sparsity. In this paper, we present sentiment analysis models that analyzing people feelings and opinions in the short text such as tweets and instant messages. Then, we illustrate the evaluation metrics used to assess the quality of the generated hierarchical structure in extracting an ideal tree.

show abstract

Section: Hierarchical Sentiment Analysis Methodsmentioning

confidence: 99%

A Study of Sentiment Analysis Approaches in Short Text

Ibrahim

Hassaballah

Ali

et al. 2021

Digital Transformation Technology

Self Cite

View full text Add to dashboard Cite

show abstract

“…A popular topic model that represents documents as mixtures of topics is the Latent Dirichlet allocation (LDA) model, which models each topic as a distribution over words. A number of recent author topic models, which merge the authors information into the topic model, have been proposed [22,11,4,25], such as the Author-Topic model [21], which discovers underlying topics conditioned on the authors information, and where each author is associated with a probability distribution over topics. The Community-Author-Recipient-Topic (CART) model [19] was proposed to extract communities by using the semantic content of a social network, and was one of the first attempts to integrate social links and content information for the purpose of community discovery.…”

Section: Related Workmentioning

confidence: 99%

Modelling user attitudes using hierarchical sentiment-topic model

Almars

Zhao

2019

Data & Knowledge Engineering

Self Cite

View full text Add to dashboard Cite

Uncovering the latent structure of various hotly discussed topics and the corresponding sentiments from different social media user groups (e.g., Twitter) is critical for helping organizations and governments understand how users feel about their services and facilities, along with the events happening around them.Although numerous research texts have explored sentiment analysis on the different aspects of a product, fewer works have focused on why users like or dislike those products. In this paper, a novel probabilistic model is proposed, namely, the Hierarchical User Sentiment Topic Model (HUSTM), to discover the hidden structure of topics and users while performing sentiment analysis in a unified way. The goal of the HUSTM is to hierarchically model the users attitudes (opinions ) using different topic and sentiment information, including the positive, negative, and neutral. The experiment results on real-world datasets show the high quality of the hierarchy obtained by the HUSTM in comparison to those discovered using other state-of-the-art techniques.

show abstract

“…A number of recent author topic models that merge the author's information into the topic model have been proposed [83,41,7,62,86]. Figure 2.3 shows the author layer integrated into the LDA model.…”

Section: User (Author) Interest Analysismentioning

confidence: 99%

“…One of the most popular areas in data mining is user (author) community analysis. A number of models that merge the author's information into the topic model have been proposed [83,53,7,91], such as the author topic model [82].…”

Section: Introductionmentioning

confidence: 99%

Structured sentiment analysis in social media

Almars¹

View full text Add to dashboard Cite

In recent years, social media platforms, such as Twitter and Webio, have become popular sources of information on the web. These platforms contain a wealth of valuable information about user opinions, user interests, events and more. People typically use these platforms to discuss different topics, share their opinions about them and engage in question-andanswer sessions. For example, regarding smartphones, users might discuss the main aspects of a smartphone, such as the overall design, battery capacity, screen size and camera. The natural hierarchical structure of those concepts is often hidden in social media. Discovering the hidden structure can helps users understand people' preference to a certain topic at different levels of granularity, and show the reasons why they prefer this topic. Over the past decade, research on hierarchical topic models has shown considerable progress. However, these studies may not always be directly applicable to social media due to the shortness and the shallow meaning of social media messages.There are three major challenges when dealing with social media texts. Firstly, compared with traditionally long texts, social media texts suffer from sparsity, and this issue may result in an incomprehensible and incorrect concept hierarchy. Secondly, social media contains useful information such as social opinions and information about users. Most existing methods perform a flat sentiment analysis on each extracted aspects independently, and ignore the concept hierarchy. In fact, we need to make the sentiment analysis finegrained in order to simultaneously extract the aspects and summarise people' opinions on those discovered aspects. Thirdly, the current models only discover the concept hierarchy ignoring the community structure of users. Maintaining the consistency of user's interest on several communities according to various topics and sentiment information is a challenging problem.In this thesis, the limitations of the existing work are addressed and effective solutions are proposed. First, in order to discover the hierarchical structure of social media content, a novel approach called the context coherence model (CCM) is proposed. It recursively top ii down: (1) organizes the concepts discussed by users in social media texts; and (2) identifies the hierarchical relations among concepts. In the CCM, a new measurement called context coherence is introduced that analyses words in social media texts and determines the similarities among them. Then, the hierarchical relationship between words is determined by recursively partitioning the whole corpus into smaller parts according to the similarity results. Finally, a merging operation is performed to find similar words, group them under the same topic and remove duplicated topics. The approach is evaluated on two real-world data sets. The experiments show that the proposed approach can effectively reveal the hidden structure in social media.Opinions are now reflected in social media on a wide range of topics: trends in pop music, fashion...

show abstract

Learning Concept Hierarchy from Short Texts Using Context Coherence

Cited by 4 publications

References 15 publications

A Study of Sentiment Analysis Approaches in Short Text

A Study of Sentiment Analysis Approaches in Short Text

Modelling user attitudes using hierarchical sentiment-topic model

Structured sentiment analysis in social media

Contact Info

Product

Resources

About