Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to compare these Twitter topics with topics from New York Times, taking into consideration topic categories and types. We also study the relation between the proportions of opinionated tweets and retweets and topic categories and types. Our comparisons show interesting and useful findings for downstream IR or DM applications.

/pdf/comparing-twitter-and-traditional-media-using-topic-models-40ux4xfa4w.pdf

Comparing twitter and traditional media using topic models

Sentiment analysis research has been started long back and recently it is one of the demanding research topics. Research activities on Sentiment Analysis in natural language texts and other media are gaining ground with full swing. But, till date, no concise set of factors has been yet defined that really affects how writers sentiment i.e., broadly human sentiment is expressed, perceived, recognized, processed, and interpreted in natural languages. The existing reported solutions or the available systems are still far from perfect or fail to meet the satisfaction level of the end users. The reasons may be that there are dozens of conceptual rules that govern sentiment and even there are possibly unlimited clues that can convey these concepts from realization to practical implementation. Therefore, the main aim of this book is to provide a feasible research platform to our ambitious researchers towards developing the practical solutions that will be indeed beneficial for our society, business and future researches as well.

A Practical Guide to Sentiment Analysis

Aspect extraction is an important task in sentiment analysis. Topic modeling is a popular method for the task. However, unsupervised topic models often generate incoherent aspects. To address the issue, several knowledge-based models have been proposed to incorporate prior knowledge provided by the user to guide modeling. In this paper, we take a major step forward and show that in the big data era, without any user input, it is possible to learn prior knowledge automatically from a large amount of review data available on the Web. Such knowledge can then be used by a topic model to discover more coherent aspects. There are two key challenges: (1) learning quality knowledge from reviews of diverse domains, and (2) making the model fault-tolerant to handle possibly wrong knowledge. A novel approach is proposed to solve these problems. Experimental results using reviews from 36 domains show that the proposed approach achieves significant improvements over state-of-the-art baselines.

/pdf/aspect-extraction-with-automated-prior-knowledge-learning-3dfrmfpd43.pdf

Aspect Extraction with Automated Prior Knowledge Learning

The veil of anonymity provided by smartphones with pre-paid SIM cards, public Wi-Fi hotspots, and distributed networks like Tor has drastically complicated the task of identifying users of social media during forensic investigations. In some cases, the text of a single posted message will be the only clue to an author’s identity. How can we accurately predict who that author might be when the message may never exceed 140 characters on a service like Twitter? For the past 50 years, linguists, computer scientists, and scholars of the humanities have been jointly developing automated methods to identify authors based on the style of their writing. All authors possess peculiarities of habit that influence the form and content of their written works. These characteristics can often be quantified and measured using machine learning algorithms. In this paper, we provide a comprehensive review of the methods of authorship attribution that can be applied to the problem of social media forensics. Furthermore, we examine emerging supervised learning-based methods that are effective for small sample sizes, and provide step-by-step explanations for several scalable approaches as instructional case studies for newcomers to the field. We argue that there is a significant need in forensics for new authorship attribution algorithms that can exploit context, can process multi-modal data, and are tolerant to incomplete knowledge of the space of all possible authors at training time.

Authorship Attribution for Social Media Forensics

Automatic text summarization, the computer-based production of condensed versions of documents, is an important technology for the information society. Without summaries it would be practically impossible for human beings to get access to the ever growing mass of information available online. Although research in text summarization is over 50 years old, some efforts are still needed given the insufficient quality of automatic summaries and the number of interesting summarization topics being proposed in different contexts by end users (“domain-specific summaries”, “opinion-oriented summaries”, “update summaries”, etc.). This paper gives a short overview of summarization methods and evaluation.

/pdf/automatic-text-summarization-past-present-and-future-12n7uz7p6h.pdf

Automatic Text Summarization: Past, Present and Future

Social media streams such as Twitter are regarded as faster first-hand sources of information generated by massive users. The content diffused through this channel, although noisy, provides important complement and sometimes even a substitute to the traditional news media reporting. In this paper, we propose a novel unsupervised approach based on topic modeling to summarize trending subjects by jointly discovering the representative and complementary information from news and tweets. Our method captures the content that enriches the subject matter by reinforcing the identification of complementary sentence-tweet pairs. To valuate the complementarity of a pair, we leverage topic modeling formalism by combining a two-dimensional topic-aspect model and a cross-collection approach in the multi-document summarization literature. The final summaries are generated by co-ranking the news sentences and tweets in both sides simultaneously. Experiments give promising results as compared to state-of-the-art baselines.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5592&context=sis_research

Joint topic modeling for event summarization across news and social media streams

In this paper, we propose a novel approach to automatic generation of summary templates from given collections of summary articles. This kind of summary templates can be useful in various applications. We first develop an entity-aspect LDA model to simultaneously cluster both sentences and words into aspects. We then apply frequent subtree pattern mining on the dependency parse trees of the clustered and labeled sentences to discover sentence patterns that well represent the aspects. Key features of our method include automatic grouping of semantically related sentence patterns and automatic identification of template slots that need to be filled in. We apply our method on five Wikipedia entity categories and compare our method with two baseline methods. Both quantitative evaluation based on human judgment and qualitative comparison demonstrate the effectiveness and advantages of our method.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1640&context=sis_research

Generating Templates of Entity Summaries with an Entity-Aspect Model and Pattern Mining

In this paper, we propose a novel approach to automatic generation of aspect-oriented summaries from multiple documents. We first develop an event-aspect LDA model to cluster sentences into aspects. We then use extended LexRank algorithm to rank the sentences in each cluster. We use Integer Linear Programming for sentence selection. Key features of our method include automatic grouping of semantically related sentences and sentence ranking based on extension of random walk model. Also, we implement a new sentence compression algorithm which use dependency tree instead of parser tree. We compare our method with four baseline methods. Quantitative evaluation based on Rouge metric demonstrates the effectiveness and advantages of our method.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5595&context=sis_research

Generating Aspect-oriented Multi-Document Summarization with Event-aspect model

A method for providing a complementary summary of news information. The method comprises retrieving a first group of relevant text sentences about an event from a first content source, such as a news media stream, and retrieving a second group of relevant text messages about the same event from a second content source, such as a social media stream or microblogs. The method analyses the retrieved text information and calculates a measure of complementarity between the text information in the first and second groups and ranks the text sentences from the first group against the text messages from the second group according to the measured complementarity between these individual text sentences and messages. Then two types of complementary summaries are generated based on different granularities of complementarity: (1 ) considering summary-level complementarity, the two summaries are generated from two groups of text in such a way that they are complementary as a whole; (2) considering sentence-level complementarity, the two summaries are produced from two groups of text with strict one-on-one complementary correspondence inside the news sentences and text messages that constitute the respective summary.

Joint topic model for cross-media news summarization

In this paper, we propose a novel approach to automatic generation of summary templates from given collections of summary articles. We first develop an entity-aspect LDA model to simultaneously cluster both sentences and words into aspects. We then apply frequent subtree pattern mining on the dependency parse trees of the clustered and labeled sentences to discover sentence patterns that well represent the aspects. Finally, we use the generated templates to construct summaries for new entities. Key features of our method include automatic grouping of semantically related sentence patterns and automatic identification of template slots that need to be filled in. Also, we implement a new sentence compression algorithm which use dependency tree instead of parser tree. We apply our method on five Wikipedia entity categories and compare our method with three baseline methods. Both quantitative evaluation based on human judgment and qualitative comparison demonstrate the effectiveness and advantages of our method.

Peng Li

Papers

Joint topic modeling for event summarization across news and social media streams

Generating Templates of Entity Summaries with an Entity-Aspect Model and Pattern Mining

Generating Aspect-oriented Multi-Document Summarization with Event-aspect model

Joint topic model for cross-media news summarization

Automatically building templates for entity summary construction