1. Articles in category: Sentiment

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    1. The benefits of sentiment data analysis

      The benefits of sentiment data analysis

      By Kgaogelo Letsebe , Portals journalist Johannesburg, 16 Mar 2017 BrandsEye CEO JP Kloppers says there are numerous benefits to the commercial applications of accurate sentiment-driven data. By definition sentiment analysis, sometimes known as opinion mining or emotion AI, refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information.

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      Mentions: Johannesburg
    2. Emotion-related query processing

      Embodiments of techniques, apparatuses and systems associated with emotion information processing are disclosed. In some embodiments, a computing system may receive an image of a person and identify an emotional state of the person, based at least in part on the image. The computing system may cause storage of the emotional state of the person in combination with other data to enable subsequent response to an emotion-related query provided to the computing system. The emotion-related query may include an emotion-related criteria and a non-emotion-related criteria and the response may be based at least in part on the emotional state in ...

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    3. Understanding patient satisfaction with received healthcare services: A natural language processing approach.

      Understanding patient satisfaction with received healthcare services: A natural language processing approach.

      Understanding patient satisfaction with received healthcare services: A natural language processing approach.

      AMIA Annu Symp Proc. 2016;2016:524-533

      Authors: Doing-Harris K, Mowery DL, Daniels C, Chapman WW, Conway M

      Abstract Important information is encoded in free-text patient comments. We determine the most common topics in patient comments, design automatic topic classifiers, identify comments ' sentiment, and find new topics in negative comments.

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    4. A Test: Which Vendor Wins at Sentiment Analysis?

      A Test: Which Vendor Wins at Sentiment Analysis?

      Businesses could benefit from early detection of negative sentiments in Enterprise Social Networks. PHOTO: Andrew Nolan Natural language processing and sentiment analysis have been popular artificial intelligence (AI) research topics for decades now. Early sentiment analysis efforts were typically applied to significant bodies of text, like movie or book reviews.

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      Mentions: Stanford Mary
    5. Prediction of advertisement preference by fusing EEG response and sentiment analysis.

      Prediction of advertisement preference by fusing EEG response and sentiment analysis.

      Prediction of advertisement preference by fusing EEG response and sentiment analysis.

      Neural Netw. 2017 Feb 16;:

      Authors: Gauba H, Kumar P, Roy PP, Singh P, Dogra DP, Raman B

      Abstract This paper presents a novel approach to predict rating of video-advertisements based on a multimodal framework combining physiological analysis of the user and global sentiment-rating available on the internet.

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      Mentions: EEG
    6. Algorithms, Vol. 10, Pages 33: Large Scale Implementations for Twitter Sentiment Classification

      Algorithms, Vol. 10, Pages 33: Large Scale Implementations for Twitter Sentiment Classification

      Algorithms 2017 , 10 (1), 33; doi:10.3390/a10010033 (registering DOI) Large Scale Implementations for Twitter Sentiment Classification Computer Engineering and Informatics Department, University of Patras, Patras 26504, Greece 2 Department of Informatics, Ionian University, Corfu 49100, Greece 3 Department of Cultural Heritage Management and New Technologies, University of Patras, Agrinio 30100, Greece 4 Computer & Informatics Engineering Department, Technological Educational Institute of Western Greece, Patras 26334, Greece * Author to whom correspondence should be addressed. Academic Editor: Bruno Carpentieri Received: 8 December 2016 / Revised: 28 February 2017 / Accepted: 1 March 2017 / Published: 4 March 2017 (This article belongs to the Special ...

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    7. Linking multiple entities associated with media content

      In one embodiment, a method includes determining that media content being viewed by a user comprises a plurality of entities, accessing information indicative of the plurality of entities, and querying a social graph of the social-networking system for social content associated with each of the plurality of entities and one or more other users of the social-networking system. The social graph includes user nodes that are each associated with a particular user of the social-networking system. The method further includes providing at least a portion of the queried social content from the social graph for display along with the information ...

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    8. Crowd-powered self-improving interactive visualanalytics for user-generated opinion data

      Embodiments relate to interacting with a collection of user opinion documents associated with a topic. One aspect includes obtaining opinion data for the collection of opinion documents associated with the topic. The opinion data includes one or more features discussed in the opinion documents, one or more key phrases included in each feature, one or more text snippets included in each feature, and at least one sentiment expressed in each text snippet. A visual interface is provided in which a feature summary view of the opinion documents acts a top level of a navigational hierarchy. The visual interface allows user ...

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    9. Method and system for achieving emotional text to speech utilizing emotion tags expressed as a set of emotion vectors

      A method and system for achieving emotional text to speech. The method includes: receiving text data; generating emotion tag for the text data by a rhythm piece; and achieving TTS to the text data corresponding to the emotion tag, where the emotion tags are expressed as a set of emotion vectors; where each emotion vector includes a plurality of emotion scores given based on a plurality of emotion categories. A system for the same includes: a text data receiving module; an emotion tag generating module; and a TTS module for achieving TTS, wherein the emotion tag is expressed as a ...

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      Mentions: TTS
    10. How should investors react when President Trump tweets about

      How should investors react when President Trump tweets about

      How should investors react when President Trump tweets about a stock? Even the President of the United States suffers from "Diminishing Returns of Influence". Trump's tweets are a catalyst for a broader discussion which can be mined for insight.

      Source: BUZZ Indexes President Trump's inauguration hasn't slowed down his Twitter activity. In a recent interview with the Wall Street Journal I was asked how Trump's tweets about specific companies affect stock prices.

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    11. Trump Tweets And Stock Sentiment

      Trump Tweets And Stock Sentiment

      Trump Tweets And Stock Sentiment Feb. 7, 2017 8:01 AM ET | How should investors react when President Trump tweets about a stock? Even the President of the United States suffers from "Diminishing Returns of Influence". Trump's tweets are a catalyst for a broader discussion which can be mined for insight. Source: BUZZ Indexes President Trump's inauguration hasn't slowed down his Twitter activity.

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    12. Automatic Construction and Global Optimization of a Multisentiment Lexicon.

      Automatic Construction and Global Optimization of a Multisentiment Lexicon.

      Automatic Construction and Global Optimization of a Multisentiment Lexicon.

      Comput Intell Neurosci. 2016;2016:2093406

      Authors: Yang X, Zhang Z, Zhang Z, Mo Y, Li L, Yu L, Zhu P

      Abstract Manual annotation of sentiment lexicons costs too much labor and time, and it is also difficult to get accurate quantification of emotional intensity. Besides, the excessive emphasis on one specific field has greatly limited the applicability of domain sentiment lexicons (Wang et al., 2010).

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      Mentions: Li L
    13. AI, Machine Learning and Sentiment Analysis Applied to Finance Seminar (London, United Kingdom - June 28-29, 2017) - Research and Markets

      By: Research and Markets via Business Wire News Releases January 31, 2017 at 11:38 AM EST AI, Machine Learning and Sentiment Analysis Applied to Finance Seminar (London, United Kingdom - June 28-29, 2017) - Research and Markets Research and Markets has announced the addition of the "AI, Machine Learning and Sentiment Analysis Applied to Finance" conference to their offering.

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    14. AI, Machine Learning and Sentiment Analysis Applied to Finance Seminar (London, United Kingdom - June 28-29, 2017) - Research and Markets

      AI, Machine Learning and Sentiment Analysis Applied to Finance Seminar (London, United Kingdom - June 28-29, 2017) - Research and Markets

      AI, Machine Learning and Sentiment Analysis Applied to Finance Seminar (London, United Kingdom - June 28-29, 2017) - Research and Markets Research and Markets has announced the addition of the "AI, Machine Learning and Sentiment Analysis Applied to Finance" conference to their offering. Technology innovations meet greatest success in business when these are entirely client focussed'.

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    15. Rezapour and Diesner to present sentiment analysis research at ICSC 2017

      Rezapour and Diesner to present sentiment analysis research at ICSC 2017

      » Rezapour and Diesner to present sentiment analysis research at ICSC 2017 Rezapour and Diesner to present sentiment analysis research at ICSC 2017 Posted: January 30, 2017 Assistant Professor » Doctoral student Shadi Rezapour and Assistant Professor Jana Diesner will present a paper at The 11th IEEE International Conference on Semantic Computing ( ICSC 2017 ), which will be held January 30 through February 1 in San Diego, California.

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    16. Sentiment Analysis Offers a Better Way to Conduct Polls

      Sentiment Analysis Offers a Better Way to Conduct Polls

      Anne Nasato Screenshot from estorm.org displaying Twitter sentiment in terms of political orientation. The majority of the polls for the 2016 Presidential Election turned out to be mistaken. One possible reason for this is that the majority of polls are based on survey questions. However, a group of engineering researchers may have found a better polling method by using the election to get an idea of popular opinion without resorting to survey questions.

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    17. Figuring out what Aussies think about Trump on Twitter is pretty difficult

      Figuring out what Aussies think about Trump on Twitter is pretty difficult

      Figuring out what Aussies think about Trump on Twitter is pretty difficult 783 Trump's election has been met with division, to say the least - but less so in Australia Image: Slate Scott Bingley, Paul Hawking for The Conversation 2017-01-22 07:00:01 UTC Follow @conversationuk Australians reacted more "positive" than "negative" to the election of Donald Trump as the next president of the United States, according to a sentiment analysis study of tweets that were posted at the time.

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    18. Optimized autocompletion of search field

      A computer receives event information associated with a user. The computer determines one or more social media contacts associated with the event, wherein the social media contacts are further associated with the user. The computer determines one or more terms utilized by the determined one or more social media contacts. The computer detects an input by the user, wherein the input includes one or more characters. The computer determines one or more autocomplete suggestions based on the one or more terms utilized by the determined one or more social media contacts.

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    19. Method and apparatus to calculate real-time customer satisfaction and loyalty metric using social media analytics

      A method and system are disclosed for providing a near-real-time measurement of sentiment and advocacy associated with user interactions within a social media environment. A first and second set of social media data, respectively associated with a first and second set of social media interactions, are processed to generate a first and second set of social network advocacy (SNA) data in near-real-time. The resulting first and second sets of SNA data are then processed to generate a first and second set of SNA Pulse (SNAP) metric data, which respectively indicate a near-real-time measurement of sentiment and advocacy for a given ...

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    1-24 of 729 1 2 3 4 ... 29 30 31 »
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