Unsupervised keyword extraction from microblog posts via hashtags

© River Publishers. Nowadays, huge amounts of texts are being generated for social networking purposes on Web. Keyword extraction from such texts like microblog posts benefits many applications such as advertising, search, and content filtering. Unlike traditional web pages, a microblog post usually has some special social feature like a hashtag that is topical in nature and generated by users. Extracting keywords related to hashtags can reflect the intents of users and thus provides us better understanding on post content. In this paper, we propose a novel unsupervised keyword extraction approach for microblog posts by treating hashtags as topical indicators. Our approach consists of two hashtag enhanced algorithms. One is a topic model algorithm that infers topic distributions biased to hashtags on a collection of microblog posts. The words are ranked by their average topic probabilities. Our topic model algorithm can not only find the topics of a collection, but also extract hashtag-related keywords. The other is a random walk based algorithm. It first builds a word-post weighted graph by taking into account posts themselves. Then, a hashtag biased random walk is applied on this graph, which guides the algorithm to extract keywords according to hashtag topics. Last, the final ranking score of a word is determined by the stationary probability after a number of iterations. We evaluate our proposed approach on a collection of real Chinese microblog posts. Experiments show that our approach is more effective in terms of precision than traditional approaches considering no hashtag. The result achieved by the combination of two algorithms performs even better than each individual algorithm

A Multi-label Text Classification Framework: Using Supervised and Unsupervised Feature Selection Strategy

Text classification, the task of metadata to documents, needs a person to take significant time and effort. Since online-generated contents are explosively growing, it becomes a challenge for manually annotating with large scale and unstructured data. Recently, various state-or-art text mining methods have been applied to classification process based on the keywords extraction. However, when using these keywords as features in the classification task, it is common that the number of feature dimensions is large. In addition, how to select keywords from documents as features in the classification task is a big challenge. Especially, when using traditional machine learning algorithms in big data, the computation time is very long. On the other hand, about 80% of real data is unstructured and non-labeled in the real world. The conventional supervised feature selection methods cannot be directly used in selecting entities from massive data. Usually, statistical strategies are utilized to extract features from unlabeled data for classification tasks according to their importance scores. We propose a novel method to extract key features effectively before feeding them into the classification assignment. Another challenge in the text classification is the multi-label problem, the assignment of multiple non-exclusive labels to documents. This problem makes text classification more complicated compared with a single label classification. For the above issues, we develop a framework for extracting data and reducing data dimension to solve the multi-label problem on labeled and unlabeled datasets. In order to reduce data dimension, we develop a hybrid feature selection method that extracts meaningful features according to the importance of each feature. The Word2Vec is applied to represent each document by a feature vector for the document categorization for the big dataset. The unsupervised approach is used to extract features from real online-generated data for text classification. Our unsupervised feature selection method is applied to extract depression symptoms from social media such as Twitter. In the future, these depression symptoms will be used for depression self-screening and diagnosis

Role of sentiment classification in sentiment analysis: a survey

Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results

An artificial intelligence driven multi-feature extraction scheme for big data detection

© 2019 IEEE. The Internet improves the speed of information dissemination, and the scale of unstructured text data is expanding and increasingly being used for mass communication. Although these large amounts of data meet the infinite demand, it is difficult to find public focus in a timely manner. Therefore, information extraction from big data has become an important research issue, and there are many published studies on big data processing at home and abroad. In this paper, we propose a multi-feature keyword extraction method, and based on this, an artificial intelligence driven big data MFE scheme is designed, then an application example of the general scheme is expanded and detailed. Taking news as the carrier, this scheme is applied to the algorithm design of hot event detection. As a result, a multi-feature fusion clustering algorithm is proposed based on user attention with two main stages. In the first stage, a multi-feature fusion model is developed to evaluate keywords, and this model combines the term frequency and part of speech features. We use it to extract keywords for representing news and events. In the second stage, we perform clustering and detect hot events in accordance with the procedure, and during the composition of news clusters, we analyze several variadic parameters in order to explore the optimal effectiveness. Then, experiments on the news corpus are conducted, and the results show that the approach presented herein performs well

Machine Learning Methods for Finding Textual Features of Depression from Publications

Depression is a common but serious mood disorder. In 2015, WHO reports about 322 million people were living with some form of depression, which is the leading cause of ill health and disability worldwide. In USA, there are approximately 14.8 million American adults (about 6.7% percent of the US population) affected by major depressive disorder. Most individuals with depression are not receiving adequate care because the symptoms are easily neglected and most people are not even aware of their mental health problems. Therefore, a depression prescreen system is greatly beneficial for people to understand their current mental health status at an early stage. Diagnosis of depressions, however, is always extremely challenging due to its complicated, many and various symptoms. Fortunately, publications have rich information about various depression symptoms. Text mining methods can discover the different depression symptoms from literature. In order to extract these depression symptoms from publications, machine learning approaches are proposed to overcome four main obstacles: (1) represent publications in a mathematical form; (2) get abstracts from publications; (3) remove the noisy publications to improve the data quality; (4) extract the textual symptoms from publications. For the first obstacle, we integrate Word2Vec with LDA by either representing publications with document-topic distance distributions or augmenting the word-to-topic and word-to-word vectors. For the second obstacle, we calculate a document vector and its paragraph vectors by aggregating word vectors from Word2Vec. Feature vectors are calculated by clustering word vectors. Selected paragraphs are decided by the similarity of their distances to feature vectors and the document vector to feature vectors. For the third obstacle, one class SVM model is trained by vectored publications, and outlier publications are excluded by distance measurements. For the fourth obstacle, we fully evaluate the possibility of a word as a symptom according to its frequency in entire publications, and local relationship with its surrounding words in a publication

Artificial and Natural Topic Detection in Online Social Networks

Online Social Networks (OSNs), such as Twitter, offer attractive means of social interactions and communications, but also raise privacy and security issues. The OSNs provide valuable information to marketing and competitiveness based on users posts and opinions stored inside a huge volume of data from several themes, topics, and subjects. In order to mining the topics discussed on an OSN we present a novel application of Louvain method for TopicModeling based on communities detection in graphs by modularity. The proposed approach succeeded in finding topics in five different datasets composed of textual content from Twitter and Youtube. Another important contribution achieved was about the presence of texts posted by spammers. In this case, a particular behavior observed by graph community architecture (density and degree) allows the indication of a topic strength and the classification of it as natural or artificial. The later created by the spammers on OSNs