An integrated semantic-based framework for intelligent similarity measurement and clustering of microblogging posts

Twitter, the most popular microblogging platform, is gaining rapid prominence as a source of information sharing and social awareness due to its popularity and massive user generated content. These include applications such as tailoring advertisement campaigns, event detection, trends analysis, and prediction of micro-populations. The aforementioned applications are generally conducted through cluster analysis of tweets to generate a more concise and organized representation of the massive raw tweets. However, current approaches perform traditional cluster analysis using conventional proximity measures, such as Euclidean distance. However, the sheer volume, noise, and dynamism of Twitter, impose challenges that hinder the efficacy of traditional clustering algorithms in detecting meaningful clusters within microblogging posts. The research presented in this thesis sets out to design and develop a novel short text semantic similarity (STSS) measure, named TREASURE, which captures the semantic and structural features of microblogging posts for intelligently predicting the similarities. TREASURE is utilised in the development of an innovative semantic-based cluster analysis algorithm (SBCA) that contributes in generating more accurate and meaningful granularities within microblogging posts. The integrated semantic-based framework incorporating TREASURE and the SBCA algorithm tackles both the problem of microblogging cluster analysis and contributes to the success of a variety of natural language processing (NLP) and computational intelligence research. TREASURE utilises word embedding neural network (NN) models to capture the semantic relationships between words based on their co-occurrences in a corpus. Moreover, TREASURE analyses the morphological and lexical structure of tweets to predict the syntactic similarities. An intrinsic evaluation of TREASURE was performed with reference to a reliable similarity benchmark generated through an experiment to gather human ratings on a Twitter political dataset. A further evaluation was performed with reference to the SemEval-2014 similarity benchmark in order to validate the generalizability of TREASURE. The intrinsic evaluation and statistical analysis demonstrated a strong positive linear correlation between TREASURE and human ratings for both benchmarks. Furthermore, TREASURE achieved a significantly higher correlation coefficient compared to existing state-of-the-art STSS measures. The SBCA algorithm incorporates TREASURE as the proximity measure. Unlike conventional partition-based clustering algorithms, the SBCA algorithm is fully unsupervised and dynamically determine the number of clusters beforehand. Subjective evaluation criteria were employed to evaluate the SBCA algorithm with reference to the SemEval-2014 similarity benchmark. Furthermore, an experiment was conducted to produce a reliable multi-class benchmark on the European Referendum political domain, which was also utilised to evaluate the SBCA algorithm. The evaluation results provide evidence that the SBCA algorithm undertakes highly accurate combining and separation decisions and can generate pure clusters from microblogging posts. The contributions of this thesis to knowledge are mainly demonstrated as: 1) Development of a novel STSS measure for microblogging posts (TREASURE). 2) Development of a new SBCA algorithm that incorporates TREASURE to detect semantic themes in microblogs. 3) Generating a word embedding pre-trained model learned from a large corpus of political tweets. 4) Production of a reliable similarity-annotated benchmark and a reliable multi-class benchmark in the domain of politics

A Heuristic Based Pre-processing Methodology for Short Text Similarity Measures in Microblogs

Short text similarity measures have lots of applications in online social networks (OSN), as they are being integrated in machine learning algorithms. However, the data quality is a major challenge in most OSNs, particularly Twitter. The sparse, ambiguous, informal, and unstructured nature of the medium impose difficulties to capture the underlying semantics of the text. Therefore, text pre-processing is a crucial phase in similarity identification applications, such as clustering and classification. This is because selecting the appropriate data processing methods contributes to the increase in correlations of the similarity measure. This research proposes a novel heuristicdriven pre-processing methodology for enhancing the performance of similarity measures in the context of Twitter tweets. The components of the proposed pre-processing methodology are discussed and evaluated on an annotated dataset that was published as part of SemEval-2014 shared task. An experimental analysis was conducted using the cosine angle as a similarity measure to assess the effect of our method against a baseline (C-Method). Experimental results indicate that our approach outperforms the baseline in terms of correlations and error rates

An Empirical Performance Evaluation of Semantic-Based Similarity Measures in Microblogging Social Media

Measuring textual semantic similarity has been a subject of intense discussion in NLP and AI for many years. A new area of research has emerged that applies semantic similarity measures within Twitter. However, the development of these measures for the semantic analysis of tweets imposes fundamental challenges. The sparsity, ambiguity, and informality present in social media are hampering the performance of traditional textual similarity measures as “tweets”, have special syntactic and semantic characteristics. This paper reviews and evaluates the performance of topological, statistical, and hybrid similarity measures, in the context of Twitter analysis. Furthermore, the performance of each measure is compared against a naïve keyword-based similarity computation method to assess the significance of semantic computation in capturing the meaning in tweets. An experiment is designed and conducted to evaluate the different measures through examining various metrics, including correlation, error rates, and statistical tests on a benchmark dataset. The potential weaknesses of semantic similarity measures in relation to Twitter applications of textual similarity assessment and the research contributions are discussed. This research highlights challenges and potential improvement areas for the semantic similarity of tweets, a resource for researchers and practitioners