3,673 research outputs found
Sentiment analysis on online social network
A large amount of data is maintained in every Social networking sites.The total data constantly gathered on these sites make it difficult for methods like use of field agents, clipping services and ad-hoc research to maintain social media data. This paper discusses the previous research on sentiment analysis
Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election
Social media has become an emerging alternative to opinion polls for public
opinion collection, while it is still posing many challenges as a passive data
source, such as structurelessness, quantifiability, and representativeness.
Social media data with geotags provide new opportunities to unveil the
geographic locations of users expressing their opinions. This paper aims to
answer two questions: 1) whether quantifiable measurement of public opinion can
be obtained from social media and 2) whether it can produce better or
complementary measures compared to opinion polls. This research proposes a
novel approach to measure the relative opinion of Twitter users towards public
issues in order to accommodate more complex opinion structures and take
advantage of the geography pertaining to the public issues. To ensure that this
new measure is technically feasible, a modeling framework is developed
including building a training dataset by adopting a state-of-the-art approach
and devising a new deep learning method called Opinion-Oriented Word Embedding.
With a case study of the tweets selected for the 2016 U.S. presidential
election, we demonstrate the predictive superiority of our relative opinion
approach and we show how it can aid visual analytics and support opinion
predictions. Although the relative opinion measure is proved to be more robust
compared to polling, our study also suggests that the former can advantageously
complement the later in opinion prediction
Implicit Sentiment Identification using Aspect based Opinion Mining
Opinion mining or sentiment analysis is the computational study of opinions or emotions towards aspects or things. The aspects are nothing but attributes or components of the individuals, events, topics, products and organizations. Opinion mining has been an active research area in Web mining and Natural Language Processing (NLP) in recent years. With the explosive growth of E-commerce, there are millions of product options available and people tend to review the viewpoint of others before buying a product. An aspect-based opinion mining approach helps in analyzing opinions about product features and attributes. This project is based on extracting aspects and related customer sentiments on tourism domain. This offers an approach to discover consumer preferences about tourism products and services using statistical opinion mining. The proposed system tries to extract both explicit aspects as well as implicit aspects from customer reviews. It thus increases the sentiment orientation of opinion. Most of the researches were based on explicit opinions of customers. This system tries to retrieve implicit sentiments. Due to the growing availability of unstructured reviews, the proposed system gives a summarized form of the information that is obtained from the reviews in order to furnish customers with pin point or crisp results.
DOI: 10.17762/ijritcc2321-8169.16049
Lifelong Learning CRF for Supervised Aspect Extraction
This paper makes a focused contribution to supervised aspect extraction. It
shows that if the system has performed aspect extraction from many past domains
and retained their results as knowledge, Conditional Random Fields (CRF) can
leverage this knowledge in a lifelong learning manner to extract in a new
domain markedly better than the traditional CRF without using this prior
knowledge. The key innovation is that even after CRF training, the model can
still improve its extraction with experiences in its applications.Comment: Accepted at ACL 2017. arXiv admin note: text overlap with
arXiv:1612.0794
Replication issues in syntax-based aspect extraction for opinion mining
Reproducing experiments is an important instrument to validate previous work
and build upon existing approaches. It has been tackled numerous times in
different areas of science. In this paper, we introduce an empirical
replicability study of three well-known algorithms for syntactic centric
aspect-based opinion mining. We show that reproducing results continues to be a
difficult endeavor, mainly due to the lack of details regarding preprocessing
and parameter setting, as well as due to the absence of available
implementations that clarify these details. We consider these are important
threats to validity of the research on the field, specifically when compared to
other problems in NLP where public datasets and code availability are critical
validity components. We conclude by encouraging code-based research, which we
think has a key role in helping researchers to understand the meaning of the
state-of-the-art better and to generate continuous advances.Comment: Accepted in the EACL 2017 SR
Cross-Cultural Comparisons of Review Aspect Importance
Previous text mining studies identified key common aspects in online restaurant reviews. However, it is not clear how important these aspects are for consumers. In this exploratory study, we used Yelp restaurant reviews on an ethnic food item, ramen noodles, and assessed the importance of each aspect to both U.S. and Japanese consumers. The results show that food and atmosphere are far more important than the other common aspects in both the U.S. and Japan. However, we found noticeable differences between consumers in the two countries regarding how the food aspect plays a role on star ratings. Both implications and a future research agenda are discussed
Facet Based Estimation Polling From Customer Reviews
Reputation-based belief systems are broadly used in e-Trade applications, and response ratings are aggregated to figure out traders’ reputation grades. The “all good reputation” problem, however, is prevalent in recent reputation systems. Reputation grades across the web are commonly high for traders and it is difficult for potential customers to choose accurate traders. This work is based on the observation that customers often express their viewpoints explicitly in free text response comments. We propose a system that figure out Comm-Trust for trust evaluation by drilling response comments. We propose multidimensional belief model for estimating reputation grades from user response comments. We propose an algorithm for mining response comments for dimension ratings and weights, combing techniques of natural language processing, opinion mining and topic modeling. This research work is mainly based on the first piece of work on trust evaluation by mining response comments
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