1. Articles in category: Sentiment

    73-96 of 771 « 1 2 3 4 5 6 7 ... 31 32 33 »
    1. Pyspark, Natural Language Processing(Sentimental Analysis), d3. by mambohayakuhusu

      Pyspark, Natural Language Processing(Sentimental Analysis), d3. by mambohayakuhusu

      Open Pyspark, Natural Language Processing(Sentimental Analysis), d3. This project is ending in 6 days and has an average bid price of $551 USD . 7 Project Description I have a project that needs developer who knows what they are doing. Here is the overview: 1. Collect tweets using tweeter streaming API, 2. Apply machine learning for sentimental analysis(NLP), (Apache spark is a must at this stage) 3.

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      Mentions: API
    2. These 19-Year-Old Engineering Students Predicted Trump's Win. Here's How.

      These 19-Year-Old Engineering Students Predicted Trump's Win. Here's How.

      These 19-Year-Old Engineering Students Predicted Trump's Win. Here's How. 2.8K Karis Hustad - Staff writer 11/9/16 @5:32pm in Education 11/9/16 @5:32pm While pollsters frantically changed their predictions as Donald Trump picked up Florida, Ohio, North Carolina (and eventually traditionally blue Wisconsin and Michigan), University of Illinois Urbana Champaign sophomores William Widjaja and Cody Pawlowski weren't surprised. They predicted he would win in June.

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    3. Message management method

      A method for managing computer based messaging involves monitoring messages transmitted within a messaging system over time; identifying related messages; and automatically analyzing the related messages using natural language analytics. The analyzing is based upon: subject of, sentiment within, and context of, each of the related messages, and frequency of transmittals of the related messages. The analyzing involves assigning at least: a first value based upon sentiment, a second value based upon content, and a third value based upon frequency, and calculating a score for the related messages as a function of the first value, second value and third value ...

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    4. #Ebook Deal/Day: Sentiment Analysis in Social Networks - $29.98 (Save 50%) Use code DEAL

      #Ebook Deal/Day: Sentiment Analysis in Social Networks - $29.98 (Save 50%) Use code DEAL

      Sentiment Analysis in Social Networks By Federico Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu Publisher: Elsevier / Morgan Kaufmann Final Release Date: October 2016 Pages: 284 The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker.

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    5. Hybrid human machine learning system and method

      Embodiments of the present invention provide a system, method, and article of hybrid human machine learning system with tagging and scoring techniques for sentiment magnitude scoring of textual passages. The combination of machine learning systems with data from human pooled language extraction techniques enable the present system to achieve high accuracy of human sentiment measurement and textual categorization of raw text, blog posts, and social media streams. This information can then be aggregated to provide brand and product strength analysis. A data processing module is configured to get streaming data and then tag the streaming data automatically using the machine ...

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    6. Configurable extractions in social media

      Disclosed are various embodiments for accessing and processing social media content. An extraction configuration comprising definitions for keywords, social networks, extraction times, and/or actions to be initiated upon a detection of a condition may be defined by a user of a site monitoring system. The defined social networks may be accessed at the defined extraction times to obtain data from a post comprising the defined keyword. The presence of some data in association with the post may initiate an action defined by the user.

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    7. Brexit Resource Centre

      Brexit Resource Centre

      Related resources Seeking your feedback Do you have ideas on how this resource centre can be more useful to you? We invite you to leave your feedback here . Sign up for updates Sign up to be notified when there are additions, changes and announcements on the Brexit Resource Centre. Updated on 27 September 2016 UK Research Factsheet 2011-2015 Resources, output, growth, impact, collaboration, mobility The UK Research Factsheet collates data from public and commercial sources since 2011.

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    8. BPU Delivers First Korean Sentiment Analysis Engine for the Cloud

      BPU Delivers First Korean Sentiment Analysis Engine for the Cloud

      BPU Delivers First Korean Sentiment Analysis Engine for the Cloud Wednesday, 21 September 2016 ( 14 minutes ago ) SEOUL, South Korea--(BUSINESS WIRE)--BPU Holdings Corporation (BPU) of Seoul, South Korea, and BPU International today announced development of ZimGo Sentiment Analysis engine for public use in Korea. ZimGo leaps over competitive natural language processing (NLP) offerings in major ways with limited beta available first in Korean.

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    9. Social Buzz Show: Jellyfish's Hannah Rainford gives the lowdown on social media listening

      Social Buzz Show: Jellyfish's Hannah Rainford gives the lowdown on social media listening

      More Social media listening is big business, so on this week's Social Buzz Show we asked Hannah Rainford, associate director of social media at Jellyfish, to talk us through why brands are investing serious money and resource into the function. Joining The Drum's head of social Adam Libonatti-Roche on the sofa, Rainford also discussed how social listening will evolve in the future.

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    10. Cross-lingual seeding of sentiment

      A contact center system can receive messages from social media sites or centers. The messages may be in a foreign language. The system can review messages by identifying content in the social media messages with negative/positive sentiment and then identify a seed term in the messages. A seed term can be a word in another language, different from the message body. The seed term is then used to find one or more other words, in the foreign language, that are correlated with the seed term. The identification of the found words in other messages can then be used to ...

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    11. Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques.

      Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques.

      Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques.

      Cognit Comput. 2016;8:757-771

      Authors: Dashtipour K, Poria S, Hussain A, Cambria E, Hawalah AY, Gelbukh A, Zhou Q

      Abstract With the advent of Internet, people actively express their opinions about products, services, events, political parties, etc., in social media, blogs, and website comments. The amount of research work on sentiment analysis is growing explosively.

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    12. Systems and methods for managing user reputations in social networking systems

      To determine and quantify the reputations of users within a social network. In an embodiment, a social networking system determines at least one category associated with a first account of a social networking system. The social networking system determines a first reputation score for the first account in the at least one category. The category may represent subject matter in which a user of the first account has an interest. The first reputation score may be based on content posted by the first account. The first reputation score may be adjusted based on an activity and the scores of others.

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    13. Product recommendation using sentiment and semantic analysis

      In an approach to determine a product rating a computer receives a user request for a product rating. The computer retrieves from on-line sources, product information on the product and analyzes the product information to determine a first product rating. The analysis includes at least a sentiment, and a trend of the sentiment. The approach includes a computer identifying products similar to the product and retrieving from on-line sources product information on similar products. A computer extracts comments on the product from the similar product information and determines an adjustment to the first product rating based on an analysis of ...

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    14. Five Social Best-Practices and Their Examples From the Food Industry

      Five Social Best-Practices and Their Examples From the Food Industry

      Top Since social media debuted, marketers have worked against a constantly shifting landscape to wrangle social data and apply it for maximum impact. What strategies are most important now, and why? Here are five best-practices, using examples from the NetBase Social Media Best-Practices Guide 2016: Restaurant Brands for context. 1. Increasing brand awareness via social promotions This tactic might sound obvious, but the most effective approach may not be.

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      Mentions: NetBase CMO
    15. Create C# Library for Sentiment and Emotion Analysis by ricklein

      Create C# Library for Sentiment and Emotion Analysis by ricklein

      Create C# Library for Sentiment and Emotion Analysis Este proyecto termina hoy y tiene una oferta promedio de $2574 USD . 2 Descripción del Proyecto This project will use the C# language, and must be a Visual Studio project that compiles under Visual Studio 2013. The project falls into the category of Natural Language Processing. The functionality performs a sentiment analysis and an emotion analysis on text. The input to the function is a string containing text content.

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      Mentions: DLL
    16. 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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    17. 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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    18. 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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    73-96 of 771 « 1 2 3 4 5 6 7 ... 31 32 33 »
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