A systematic study on predicting depression using text analytics

Social Networking Sites (SNS) provides online communication among groups but somehow it is affecting the status of mental health. For adolescents with limited social media friends and using internet for communication purposes predicted less depression, whereas  non-communication desire reveals more depression and anxiety disorder. Social media posts and comments provide a rich source of text data for academic research. In this paper, we have discussed various text analytical approaches to predict depression among users through the sharing of online ideas over such websites. This paper presents a  comprehensive review for predicting depression disorder by various text analytics approaches. This paper also presents the summary of results obtained by some researchers available in literature to predict MajorDepressive Disorder (MDD). In future research, enable self-monitoring of health status of each individuals which may help to increase well-being of an identity.Keywords: Social Networking Sites; Sentiment Analysis; Machine Learning; Support Vector Machine

A novel concept-level approach for ultra-concise opinion summarization

The Web 2.0 has resulted in a shift as to how users consume and interact with the information, and has introduced a wide range of new textual genres, such as reviews or microblogs, through which users communicate, exchange, and share opinions. The exploitation of all this user-generated content is of great value both for users and companies, in order to assist them in their decision-making processes. Given this context, the analysis and development of automatic methods that can help manage online information in a quicker manner are needed. Therefore, this article proposes and evaluates a novel concept-level approach for ultra-concise opinion abstractive summarization. Our approach is characterized by the integration of syntactic sentence simplification, sentence regeneration and internal concept representation into the summarization process, thus being able to generate abstractive summaries, which is one the most challenging issues for this task. In order to be able to analyze different settings for our approach, the use of the sentence regeneration module was made optional, leading to two different versions of the system (one with sentence regeneration and one without). For testing them, a corpus of 400 English texts, gathered from reviews and tweets belonging to two different domains, was used. Although both versions were shown to be reliable methods for generating this type of summaries, the results obtained indicate that the version without sentence regeneration yielded to better results, improving the results of a number of state-of-the-art systems by 9%, whereas the version with sentence regeneration proved to be more robust to noisy data.This research work has been partially funded by the University of Alicante, Generalitat Valenciana, Spanish Government and the European Commission through the projects, “Tratamiento inteligente de la información para la ayuda a la toma de decisiones” (GRE12-44), “Explotación y tratamiento de la información disponible en Internet para la anotación y generación de textos adaptados al usuario” (GRE13-15), DIIM2.0 (PROMETEOII/2014/001), ATTOS (TIN2012-38536-C03-03), LEGOLANG-UAGE (TIN2012-31224), SAM (FP7-611312), and FIRST (FP7-287607)

Performance of Opinion Summarization towards Extractive Summarization

Opinion summarization summarizes opinion in texts while extractive summarization summarizes texts without considering opinion in the texts. Can opinion summarization be used to produce a better extractive summary? This paper proposes to determine the effectiveness of opinion summarization generation against extractive text summarization. Sentiment that includes emotion which indicates whether a sentence may be positive, negative or neutral is considered. Sentences that have strong sentiment, either positive or negative are deemed important in text summarization to capture the sentiments in a story text. Thus, a comparative study is conducted on two types of summarizations; opinion summarization using the proposed method, which uses two different sentiment lexicons: VADER and SentiWordNet against extractive summarization using established methods: Luhn, Latent Semantic Analysis (LSA) and LexRank. An experiment was performed on 20 news stories, comparing summaries generated by the proposed opinion summarization method against the summaries generated by established extractive summarization methods. From the experiment, the VADER sentiment analyzer produced the best score of 0.51 when evaluated against the LSA method using ROUGE-1 metric. This implies that opinion summarization converges with extractive summarization

COMPENDIUM: a text summarisation tool for generating summaries of multiple purposes, domains, and genres

In this paper, we present a Text Summarisation tool, compendium, capable of generating the most common types of summaries. Regarding the input, single- and multi-document summaries can be produced; as the output, the summaries can be extractive or abstractive-oriented; and finally, concerning their purpose, the summaries can be generic, query-focused, or sentiment-based. The proposed architecture for compendium is divided in various stages, making a distinction between core and additional stages. The former constitute the backbone of the tool and are common for the generation of any type of summary, whereas the latter are used for enhancing the capabilities of the tool. The main contributions of compendium with respect to the state-of-the-art summarisation systems are that (i) it specifically deals with the problem of redundancy, by means of textual entailment; (ii) it combines statistical and cognitive-based techniques for determining relevant content; and (iii) it proposes an abstractive-oriented approach for facing the challenge of abstractive summarisation. The evaluation performed in different domains and textual genres, comprising traditional texts, as well as texts extracted from the Web 2.0, shows that compendium is very competitive and appropriate to be used as a tool for generating summaries.This research has been supported by the project “Desarrollo de Técnicas Inteligentes e Interactivas de Minería de Textos” (PROMETEO/2009/119) and the project reference ACOMP/2011/001 from the Valencian Government, as well as by the Spanish Government (grant no. TIN2009-13391-C04-01)

Evaluation Techniques and Graph-Based Algorithms for Automatic Summarization and Keyphrase Extraction

Automatic text summarization and keyphrase extraction are two interesting areas of research which extend along natural language processing and information retrieval. They have recently become very popular because of their wide applicability. Devising generic techniques for these tasks is challenging due to several issues. Yet we have a good number of intelligent systems performing the tasks. As different systems are designed with different perspectives, evaluating their performances with a generic strategy is crucial. It has also become immensely important to evaluate the performances with minimal human effort. In our work, we focus on designing a relativized scale for evaluating different algorithms. This is our major contribution which challenges the traditional approach of working with an absolute scale. We consider the impact of some of the environment variables (length of the document, references, and system-generated outputs) on the performance. Instead of defining some rigid lengths, we show how to adjust to their variations. We prove a mathematically sound baseline that should work for all kinds of documents. We emphasize automatically determining the syntactic well-formedness of the structures (sentences). We also propose defining an equivalence class for each unit (e.g. word) instead of the exact string matching strategy. We show an evaluation approach that considers the weighted relatedness of multiple references to adjust to the degree of disagreements between the gold standards. We publish the proposed approach as a free tool so that other systems can use it. We have also accumulated a dataset (scientific articles) with a reference summary and keyphrases for each document. Our approach is applicable not only for evaluating single-document based tasks but also for evaluating multiple-document based tasks. We have tested our evaluation method for three intrinsic tasks (taken from DUC 2004 conference), and in all three cases, it correlates positively with ROUGE. Based on our experiments for DUC 2004 Question-Answering task, it correlates with the human decision (extrinsic task) with 36.008% of accuracy. In general, we can state that the proposed relativized scale performs as well as the popular technique (ROUGE) with flexibility for the length of the output. As part of the evaluation we have also devised a new graph-based algorithm focusing on sentiment analysis. The proposed model can extract units (e.g. words or sentences) from the original text belonging either to the positive sentiment-pole or to the negative sentiment-pole. It embeds both (positive and negative) types of sentiment-flow into a single text-graph. The text-graph is composed with words or phrases as nodes, and their relations as edges. By recursively calling two mutually exclusive relations the model builds the final rank of the nodes. Based on the final rank, it splits two segments from the article: one with highly positive sentiment and the other with highly negative sentiments. The output of this model was tested with the non-polar TextRank generated output to quantify how much of the polar summaries actually covers the fact along with sentiment

Multidimensional opinion mining from social data

Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This thesis focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm, and irony, from user-generated content represented across multiple social media platforms and in various media formats, like textual, visual, and audio. Mining people’s social opinions from social sources, such as social media platforms and newswires commenting sections, is a valuable business asset that can be utilised in many ways and in multiple domains, such as Politics, Finance, and Government. The main objective of this research is to investigate how a multidimensional approach to Social Opinion Mining affects fine-grained opinion search and summarisation at an aspect-based level and whether such a multidimensional approach outperforms single dimension approaches in the context of an extrinsic human evaluation conducted in a real-world context: the Malta Government Budget, where five social opinion dimensions are taken into consideration, namely subjectivity, sentiment polarity, emotion, irony, and sarcasm. This human evaluation determines whether the multidimensional opinion summarisation results provide added-value to potential end-users, such as policy-makers and decision-takers, thereby providing a nuanced voice to the general public on their social opinions on topics of a national importance. Results obtained indicate that a more fine-grained aspect-based opinion summary based on the combined dimensions of subjectivity, sentiment polarity, emotion, and sarcasm or irony is more informative and more useful than one based on sentiment polarity only. This research contributes towards the advancement of intelligent search and information retrieval from social data and impacts entities utilising Social Opinion Mining results towards effective policy formulation, policy-making, decision-making, and decision-taking at a strategic level

Summarizing Opinions in Blog Threads

In this paper we present an approach to summarizing positive and negative opinions in blog threads. We first run a sentiment analysis system and consequently pass its output through a standard LSA-based text summarization system. Further on, we evaluate our approach and present the results obtained, which we believe are promising in the context of multi-document text summarization. Finally, we discuss the main issues in applying standard text summarization techniques to the slightly different task of summarizing opinions in blog threads.JRC.G.2-Global security and crisis managemen