90 results about "Sentiment score" patented technology

The sentimental significance of a group of historical documents related to a topic is assessed with respect to change in an extrinsic metric for the topic. A unique sentiment binding label is included to the content of actions documents that are determined to have sentimental significance and the group of documents is inserted into a historical document sentiment vector space for the topic. Action areas in the vector space are defined from the locations of action documents and singular sentiment vector may be created that describes the cumulative action area. Newly published documents are sentiment-scored by semantically comparing them to documents in the space and/or to the singular sentiment vector. The sentiment scores for the newly published documents are supplemented by human sentiment assessment of the documents and a sentiment time decay factor is applied to the supplemented sentiment score of each newly published documents. User queries are received and a set of sentiment-ranked documents is returned with the highest age-adjusted sentiment scores.

Performing detailed sentiment analysis includes generating a first sentiment score for a first entity based on a content source. The first sentiment score is generated with respect to a first dimension. A second sentiment score for the first entity is generated based on the content source. The second sentiment score is generated with respect to a second dimension.

A method for estimating a sentiment conveyed by the content of information sources towards an entity is presented. The sentiment is obtained with respect to a query context that may be specified, e.g. by specific terms or expressions, like a product or service name. A sentiment dictionary having a plurality of sentiment terms is provided, wherein each sentiment term has assigned a sentiment value, and at least one of said sentiment terms is associated to a group context. Text documents are screened for occurrences of sentiment terms that are associated to a group context corresponding to the query context. Calculating a sentiment score value is performed as a function of the occurrences of sentiment terms having a similar or same group context as the query context. The method may be carried out automatically without manual analysis of the actual semantic content of the text documents under consideration.

The invention relates to an image evaluation algorithm based on microblog sentiment analysis. The algorithm includes the following steps that microblog client information is collected; secondly, relative microblog keyword database is constructed; thirdly, a microblog sentiment score is calculated, and microblog comment sentiment tendency types are judged; fourthly, microblog comment types are divided and related and matched with enterprise service events; fifthly, an image core is calculated. Microblog comments, related to a power grid, of users serve as research objects, the method combining the text mining technology and sentiment analysis is adopted, the enterprise image scorning algorithm based on microblog sentiment analysis is built, and a strong technical means is provided for power supply enterprises to judge images and take pertinent measures.

A method including identifying an emotion identifier from a message using an emotion identifier table is provided. The method includes separating emotion identifier from the message, splitting a portion into multiple tokens delimited by a space or a punctuation mark, and determining a score for the message based on an emotion score in the emotion identifier table, and on at least one of the multiple tokens. The method includes adding a token from the multiple tokens to the emotion identifier table based on a contribution from the token to the message score, associating a sentiment score for the token in the emotion identifier table based on the contribution of the token to the message score, and modifying an emotion score of the token when the token is already included in the emotion identifier table based on the contribution of the token to the message sentiment score.

Certain aspects of the present disclosure provide techniques for generating a replacement sentence with the same or similar meaning but a different sentiment than an input sentence. The method generally includes receiving a request for a replacement sentence and iteratively determining a next word of the replacement sentence word-by-word based on an input sentence. Iteratively determining the next word generally includes evaluating a set of words of the input sentence using a language model configured to output candidate sentences and evaluating the candidate sentences using a sentiment model configured to output sentiment scores for the candidates sentences. Iteratively determining the next word further includes calculating convex combinations for the candidate sentences and selecting an ending word of one of the candidate sentences as the next word of the replacement sentence. The method further includes transmitting the replacement sentence in response to the request for the replacement sentence.

A content evaluation system is described herein that empowers end users and organizations to share their interpretation of an automatically generated sentiment score. The system provides a control that a user can move to indicate agreement or disagreement with an automatic score. The system adds metadata to a revised score based on the user's feedback that tracks information about the user to consider different demographic contexts. The system performs rescoring with the user-provided scores with contextual consideration, and then exposes the rescored values on context specific endpoints. The system provides a crowd-sourcing approach that scales extremely well, adds more accuracy because individuals within known demographic categories/contexts do the scoring, and generates value-added data products that can be sold/re-sold.

In one exemplary embodiment, a method includes the step of obtaining a digital image of a user with a user-facing camera of a computing device. It is determined that the digital image includes a frontal image of the user. A user-sentiment score is calculated based on at least one attribute of the frontal image. A user engagement value is determined with respect to a portion of a display of the computing device. At least one of the frontal image of the user, the user-sentiment score or the gaze position of the user is communicated to an external server process or an application operating in the computing device.

A method for classifying information sources and content based on credibility, reliability, or trust. A content item describing an event is retrieved from an information provider and parsed for links. Each link is evaluated and attributed a sentiment score. The same event is identified in a set of know sources and an event score is calculated based on the credibility of each of the known sources. Finally, the content item is ranked based on the event and sentiment scores.

One embodiment provides a method, including: accessing, for an identified individual, a sentiment profile comprising at least one average sentiment score, wherein the sentiment profile is generated by analyzing interactions of the identified individual with other individuals and wherein the sentiment profile reflects the identified individual's personality in interactions with other individuals; receiving input corresponding to a current interaction between the identified individual and at least one other individual; generating a current sentiment score for the identified individual corresponding to the current interaction using a sentiment analysis tool; determining a sentiment intent of the identified individual corresponding to the current interaction, wherein the determining a sentiment intent comprises comparing the current sentiment score to the at least one average sentiment score from the sentiment profile; and providing to a user, feedback of the determined sentiment intent of the identified individual.

Systems and methods are provided for displaying received publishing content on a web page along with one or more user elements by which one or more users may submit sentiment input or textual input in relation to the received publishing content or a subportion of the received publishing content; receiving a user input related to the displayed publishing content displayed on the web page, the user input including an identification of a subportion of the displayed publishing content and a sentiment input or a textual input; analyzing any user input received from each of one or more of the plurality of users in relation to the subportion; computing a sentiment score based on analysis of the analyzed user inputs received from each of one or more of the plurality of users in relation to the subportion; and displaying indicia representing the sentiment score computed for the subportion.

The invention discloses a text sentiment analysis method and device, a computer device and a computer readable storage medium. The method comprises the following steps: extracting a plurality of target articles from a preset corpus by utilizing a preset extraction rule; carrying out sentiment classification on the statement of each target article by utilizing a pre-established sentiment word segmentation dictionary; according to the sentiment classification result of the sentiment, carrying out sentiment scoring on the sentiment of each target article; obtaining an emotion classification result of each target article based on the emotion scoring condition of the statement in each target article; processing each target article in a preset mode to obtain text data after word segmentation; obtaining training data with emotion classification labels according to the text data of each target article and the emotion classification result of each target article, and obtaining an emotion classification model through training based on the training data; and performing sentiment classification on a to-be-processed article by utilizing the sentiment classification model. Article emotion can beaccurately analyzed and classified.