1. Articles in category: Sentiment

    49-72 of 727 « 1 2 3 4 5 6 ... 29 30 31 »
    1. A Piece of My Mind: A Sentiment Analysis Approach for Online Dispute Detection. (arXiv:1606.05704v1 [cs.CL])

      We investigate the novel task of online dispute detection and propose a sentiment analysis solution to the problem: we aim to identify the sequence of sentence-level sentiments expressed during a discussion and to use them as features in a classifier that predicts the DISPUTE/NON-DISPUTE label for the discussion as a whole.

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    2. Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora. (arXiv:1606.02820v1 [cs.CL])

      A word's sentiment depends on the domain in which it is used. Computational social science research thus requires sentiment lexicons that are specific to the domains being studied. We combine domain-specific word embeddings with a label propagation framework to induce accurate domain-specific sentiment lexicons using small sets of seed words.

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    3. Multilingual Visual Sentiment Concept Matching. (arXiv:1606.02276v1 [cs.CL])

      The impact of culture in visual emotion perception has recently captured the attention of multimedia research. In this study, we pro- vide powerful computational linguistics tools to explore, retrieve and browse a dataset of 16K multilingual affective visual concepts and 7.3M Flickr images. First, we design an effective crowdsourc- ing experiment to collect human judgements of sentiment connected to the visual concepts.

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    4. Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification. (arXiv:1606.01614v1 [cs.CL])

      In recent years deep neural networks have achieved great success in sentiment classification for English, thanks in part to the availability of copious annotated resources. Unfortunately, most other languages do not enjoy such an abundance of annotated data for sentiment analysis. To combat this problem, we propose the Adversarial Deep Averaging Network (ADAN) to transfer sentiment knowledge learned from labeled English data to low-resource languages where only unlabeled data exists.

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    5. Measuring patient-perceived quality of care in US hospitals using Twitter [original Research]

      Measuring patient-perceived quality of care in US hospitals using Twitter [original Research]

      Introduction Over the past decade, patient experiences have drawn increasing interest, highlighting the importance of incorporating patients’ needs and perspectives into care delivery. 1 , 2 With healthcare becoming more patient centred and outcome and value driven, healthcare stakeholders need to be able to measure, report and improve outcomes that are meaningful to patients.

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      Mentions: Amazon Bayes Medicare
    6. New Directions in Financial Analytics Conference: AI, Machine Learning and Sentiment Analysis Applied to Finance (London, Uk - July 14th & 15th, 2016) - Research and Markets

      New Directions in Financial Analytics Conference: AI, Machine Learning and Sentiment Analysis Applied to Finance (London, Uk - July 14th & 15th, 2016) - Research and Markets

      Union Carbide New Directions in Financial Analytics Conference: AI, Machine Learning and Sentiment Analysis Applied to Finance (London, UK - July 14th & 15th, 2016) - Research and Markets New Directions in Financial Analytics Conference: AI, Machine Learning and Sentiment Analysis Applied to Finance (London, UK - July 14th & 15th, 2016) - Research and Markets 05/30/2016 New Directions in Financial Analytics Conference: AI, Machine Learning and Sentiment Analysis Applied to Finance (London, UK - July 14th & 15th, 2016) - Research and Markets Research and Markets has announced the addition of the "New Directions in Financial Analytics: AI, Machine Learning and Sentiment Analysis Applied to Finance ...

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    7. New Directions in Financial Analytics Conference: AI, Machine Learning and Sentiment Analysis Applied to Finance (London, Uk - July 14th & 15th, 2016) - Research and Markets

      New Directions in Financial Analytics Conference: AI, Machine Learning and Sentiment Analysis Applied to Finance (London, Uk - July 14th & 15th, 2016) - Research and Markets

      DUBLIN--(BUSINESS WIRE)-- Research and Markets has announced the addition of the "New Directions in Financial Analytics: AI, Machine Learning and Sentiment Analysis Applied to Finance (London, UK - July 14th & 15th, 2016)" conference to their offering. AI and Machine Learning have emerged as a central aspect of analytics which is applied to multiple domains.

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      Mentions: London Dublin NLP
    8. New Directions in Financial Analytics Conference: AI, Machine Learning and Sentiment Analysis Applied to Finance (London, Uk - July 14th & 15th, 2016) - Research and Markets

      May 30, 2016 3:14pm Comments Share: DUBLIN--(BUSINESS WIRE)-- Research and Markets has announced the addition of the "New Directions in Financial Analytics: AI, Machine Learning and Sentiment Analysis Applied to Finance (London, UK - July 14th & 15th, 2016)" conference to their offering. AI and Machine Learning have emerged as a central aspect of analytics which is applied to multiple domains.

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      Mentions: London Dublin NLP
    9. New Directions in Financial Analytics Conference: AI, Machine Learning Sentiment Analysis Applied to Finance (London, Uk - July 14th & 15th, 2016) - -

      New Directions in Financial Analytics Conference: AI, Machine Learning Sentiment Analysis Applied to Finance (London, Uk - July 14th & 15th, 2016) - -

      DUBLIN--(BUSINESS WIRE)--Research and Markets has announced the addition of the "New Directions in Financial Analytics: AI, Machine Learning and Sentiment Analysis Applied to Finance (London, UK - July 14th & 15th, 2016)" conference to their offering. AI and Machine Learning have emerged as a central aspect of analytics which is applied to multiple domains. AI and Machine Learning, Pattern classifiers and natural language processing (NLP) underpin Sentiment Analysis (SA); SA is a technology »

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      Mentions: London Dublin
    10. IBM Introduces New Watson Features to the Twilio Marketplace

      IBM Introduces New Watson Features to the Twilio Marketplace

      IBM Introduces New Watson Features to the Twilio Marketplace By Jef Cozza / Sci-Tech Today PUBLISHED: 27 2016 Big Blue is teaming up with cloud communication platform Twilio to introduce two new offerings, IBM Watson Message Sentiment and IBM Watson Message Insights. Twilio, which functions as a communication tool for developers and businesses, will make both offerings available as add-ons through its recently announced marketplace, according to IBM.

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    11. IBM Introduces New Watson Features to the Twilio Marketplace

      IBM Introduces New Watson Features to the Twilio Marketplace

      IBM Introduces New Watson Features to the Twilio Marketplace By Jef Cozza / NewsFactor Network PUBLISHED: 27 2016 Big Blue is teaming up with cloud communication platform Twilio to introduce two new offerings, IBM Watson Message Sentiment and IBM Watson Message Insights. Twilio, which functions as a communication tool for developers and businesses, will make both offerings available as add-ons through its recently announced marketplace, according to IBM.

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    12. IBM Introduces New Watson Features to the Twilio Marketplace | Sci

      IBM Introduces New Watson Features to the Twilio Marketplace | Sci

      For Half the Cost Enterprise Cloud Computing On Force.com IBM Introduces New Watson Features to the Twilio Marketplace By Jef Cozza / Sci-Tech Today PUBLISHED: 27 2016 Big Blue is teaming up with cloud communication platform Twilio to introduce two new offerings, IBM Watson Message Sentiment and IBM Watson Message Insights.

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    13. IBM Introduces New Watson Features to the Twilio Marketplace

      IBM Introduces New Watson Features to the Twilio Marketplace

      IBM Introduces New Watson Features to the Twilio Marketplace By Jef Cozza / CRM Daily PUBLISHED: 27 2016 Big Blue is teaming up with cloud communication platform Twilio to introduce two new offerings, IBM Watson Message Sentiment and IBM Watson Message Insights. Twilio, which functions as a communication tool for developers and businesses, will make both offerings available as add-ons through its recently announced marketplace, according to IBM.

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    14. IBM advances Watson Services by offering on Twilio Marketplace

      About CTR IBM advances Watson Services by offering on Twilio Marketplace IBM Corp. announced Tuesday that it was joining with Twilio, a cloud communications platform for developers, to introduce two new offerings, IBM Watson Message Sentiment and IBM Watson Message Insights, which will be available as add-ons in Twilio’s recently announced Marketplace.

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    15. Sentiment Analysis and Social Cognition Engine (SEANCE): An automatic tool for sentiment, social cognition, and social-order analysis.

      Sentiment Analysis and Social Cognition Engine (SEANCE): An automatic tool for sentiment, social cognition, and social-order analysis.

      Sentiment Analysis and Social Cognition Engine (SEANCE): An automatic tool for sentiment, social cognition, and social-order analysis.

      Behav Res Methods. 2016 May 18;

      Authors: Crossley SA, Kyle K, McNamara DS

      Abstract This study introduces the Sentiment Analysis and Cognition Engine (SEANCE), a freely available text analysis tool that is easy to use, works on most operating systems (Windows, Mac, Linux), is housed on a user's hard drive (as compared to being accessed via an Internet interface), allows for batch processing of text files, includes negation and part-of-speech (POS) features, and reports on thousands of lexical categories and 20 component ...

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    16. Enhanced Twitter Sentiment Classification Using Contextual Information. (arXiv:1605.05195v1 [cs.SI])

      The rise in popularity and ubiquity of Twitter has made sentiment analysis of tweets an important and well-covered area of research. However, the 140 character limit imposed on tweets makes it hard to use standard linguistic methods for sentiment classification. On the other hand, what tweets lack in structure they make up with sheer volume and rich metadata. This metadata includes geolocation, temporal and author information.

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    17. Reconciling detailed transaction feedback

      Reconciling detailed transaction feedback by detecting a rating of a transaction, where the rating indicates a negative experience, mining the sentiment of words in feedback text that is included with or as part of the rating to detect whether the words indicate positive sentiment or negative sentiment, responsive to determining that the words in the feedback text indicate that the feedback text connotes a positive sentiment, adjusting the rating of the transaction. The mining may include testing words in the feedback text to detect whether the words indicate positive sentiment or negative sentiment by calculating a sentiment score.

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    18. System and method for analysing natural language

      A computer implemented method for analyzing natural language to determine a sentiment between two entities discussed in the natural language, comprising the following steps: receiving the natural language at a processing circuitry; analyzing the natural language to determine a syntactic representation which shows syntactic constituents of the analyzed natural language and to determine a sentiment score of each constituent; determining which constituents link the two entities; and calculating an overall sentiment score for the sentiment between the two entities by processing the sentiment score of each constituent of the constituents determined to link the two entities.

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    19. Stance and Sentiment in Tweets. (arXiv:1605.01655v1 [cs.CL])

      We can often detect from a person's utterances whether he/she is in favor of or against a given target entity -- their stance towards the target. However, a person may express the same stance towards a target by using negative or positive language. Here for the first time we present a dataset of tweet--target pairs annotated for both stance and sentiment. The targets may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets.

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    20. Modeling Rich Contexts for Sentiment Classification with LSTM. (arXiv:1605.01478v1 [cs.CL])

      Sentiment analysis on social media data such as tweets and weibo has become a very important and challenging task. Due to the intrinsic properties of such data, tweets are short, noisy, and of divergent topics, and sentiment classification on these data requires to modeling various contexts such as the retweet/reply history of a tweet, and the social context about authors and relationships.

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    49-72 of 727 « 1 2 3 4 5 6 ... 29 30 31 »
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