Entity-centric topic-oriented opinion summarization in twitter

Meng, Xinfan; Wei, Furu; Liu, Xiaohua; Zhou, Ming; Li, Sujian; Wang, Houfeng

doi:10.1145/2339530.2339592

Cited by 85 publications

(47 citation statements)

References 17 publications

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“…Characterizing an attitude as positive, negative or neutral toward a topic is known as sentiment analysis. Most of the contribution in the field focuses on finding sentiments in the tweet level [1,5,13,25], some of them suggest aggregating the sentiments as a simple sum [4,7,17,22,24], while the problem of the reputation of an entity has not been specifically addressed. Natural language processing is a well-established research area in computer science.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Frequent Named Entities-Based Approach for Interpreting Reputation in Twitter

et al. 2018

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Twitter is a social network that provides a powerful source of data. The analysis of those data offers many challenges among those stands out the opportunity to find reputation of a product, a person or any other entity of interest. Several approaches for sentiment analysis have been proposed in the literature to assess the general opinion expressed in tweets on an entity. Nevertheless, these methods aggregate sentiment scores retrieved from tweets, which is a static view to evaluate the overall reputation of an entity. The reputation of an entity is not static; entities collaborate with each other, and they get involved in different events over time. A simple aggregation of sentiment scores is then not sufficient to represent this dynamism. In this paper, we present a new approach to determine the reputation of an entity on the basis of the set of events in which it is involved. To achieve this, we propose a new sampling method driven by a tweet weighting measure to give a better quality and summary of the target entity. We introduce the concept of Frequent Named Entities to determine the events involving the target entity. Our evaluation achieved for different entities shows that 90% of the reputation of an entity originates from the events it is involved in and the breakdown into events allows interpreting the reputation in a transparent and self-explanatory way.

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Section: Related Workmentioning

confidence: 99%

“…In [17], different entities are further classified into topics (using hashtags) and the overall opinion is summarized based on the different topics. In contrast to both approaches, we are interested in finding the reputation of an entity in Twitter, which is based on news, events and activities.…”

Section: Related Workmentioning

confidence: 99%

A Frequent Named Entities-Based Approach for Interpreting Reputation in Twitter

et al. 2018

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“…Most prior works are on event-level or topic-level summarization. Typically, the first step is to cluster posts into sub-events (Chakrabarti and Punera, 2011;Duan et al, 2012;Shen et al, 2013) or subtopics (Long et al, 2011;Rosa et al, 2011;Meng et al, 2012), and then the second step generates summary for each cluster.…”

Section: Related Workmentioning

confidence: 99%

Using Content-level Structures for Summarizing Microblog Repost Trees

Gao²,

Wei³

et al. 2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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A microblog repost tree provides strong clues on how an event described therein develops. To help social media users capture the main clues of events on microblogging sites, we propose a novel repost tree summarization framework by effectively differentiating two kinds of messages on repost trees called leaders and followers, which are derived from contentlevel structure information, i.e., contents of messages and the reposting relations. To this end, Conditional Random Fields (CRF) model is used to detect leaders across repost tree paths. We then present a variant of random-walk-based summarization model to rank and select salient messages based on the result of leader detection. To reduce the error propagation cascaded from leader detection, we improve the framework by enhancing the random walk with adjustment steps for sampling from leader probabilities given all the reposting messages. For evaluation, we construct two annotated corpora, one for leader detection, and the other for repost tree summarization. Experimental results confirm the effectiveness of our method.

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“…There are several research papers discussing sentiment analysis via lexicon-based approaches [15,20,37,43,69,73,110]. Ding et al in [20] introduce a holistic lexicon-based approach to solving this problem by exploiting external evidences and the linguistic conventions of natural language expressions.…”

Section: Sentiment Analysis On Social Networkmentioning

confidence: 99%

“…Meng et al in [69] present an entity-centric topic-based opinion summarization framework in Twitter. The topic is detected from hashtags-human annotated tags for providing additional context and metadata to messages.…”

Section: Sentiment Analysis On Social Networkmentioning

confidence: 99%

Event detection in social networks

Unankard¹

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Micro-blog services such as Twitter generate a large amount of messages carrying event information and users' opinions over a wide range of topics. The events discussed on social networks can be associated with topics, locations, and time periods. The events can be a variety, such as celebrities or political affairs, local social events, accidents, protests, or natural disasters. Messages are posted by users after they have experienced or witnessed the events happening in the real-world and they want to share their experiences immediately. People also express themselves spontaneously with respect to the social events in their social networks. Alternatively, policy-makers may want to know the feelings of users for a particular event to make informed decisions. With the increasing number of real-world events that are originated and discussed over social networks, event detection and tracking is becoming a compelling research issue. However, the traditional approaches to event detection and event tracking on large text streams are not applicable because of the following problems. First, they are not designed to deal with a large number of short and noisy messages. Second, social networks contain network structures such as friends, followers, replies, and re-tweets. Third, social network messages are associated with locations, which can be either senders' current locations or event locations. Fourth, each message is also associated with a timestamp. Messages often contain revealing and timely event information however, traditional text processing approaches assume documents are non-temporal. Moreover, given a particular time frame and a location the user is interested in, events that occurred in the given time frame from the chosen area are more valuable than others. Finding localized events has not been well studied yet.The goals of this thesis are to: (1) identify subsistent problems and challenges in event detection and tracking in streaming micro-blog text, (2) design approaches for event detection and event tracking in social networks, (3) design approaches for sentiment analysis for given event topics, and (4) evaluate the proposed approaches in real-world streaming datasets.In this thesis, our research is considered in three parts. Firstly, in order to detect emerging events from a large number of short and noisy messages, we propose an approach for the early detection of emerging hotspot events in social networks with location sensitivity. An algorithm is designed for slang conversion, synonym expansion and conceptual similarity to provide a rich semantic context for measuring message similarity to improve clustering results. We consider the message-mentioned locations for identifying the locations of events. In our approach, we identify strong correlations between user locations and event locations in detecting the hotspot emerging events. A sliding window manager is used to keep track of messages arriving in the system. The size of the sliding window is defined as the time interval. We evaluate our...

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Entity-centric topic-oriented opinion summarization in twitter

Cited by 85 publications

References 17 publications

A Frequent Named Entities-Based Approach for Interpreting Reputation in Twitter

A Frequent Named Entities-Based Approach for Interpreting Reputation in Twitter

Using Content-level Structures for Summarizing Microblog Repost Trees

Event detection in social networks

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