Abstract: A set of comments associated with an item of media content is identified. A set of sentiment scores associated with the set of comments is generated, wherein each sentiment score indicates a type of sentiment expressed in a comment. The sentiment profile is generated responsive at least in part to the set of sentiment scores, the sentiment profile indicating the types of sentiment expressed in comments associated with the item of media content and stored in association with the item of media content.

Abstract: A set of comments associated with an item of media content is identified. A set of sentiment scores associated with the set of comments is generated, wherein each sentiment score indicates a type of sentiment expressed in a comment. The sentiment profile is generated responsive at least in part to the set of sentiment scores, the sentiment profile indicating the types of sentiment expressed in comments associated with the item of media content and stored in association with the item of media content.

Abstract: A set of comments associated with an item of media content is identified. A set of sentiment scores associated with the set of comments is generated, wherein each sentiment score indicates a type of sentiment expressed in a comment. The sentiment profile is generated responsive at least in part to the set of sentiment scores, the sentiment profile indicating the types of sentiment expressed in comments associated with the item of media content and stored in association with the item of media content.

Abstract: A set of comments associated with an item of media content is identified. A set of sentiment scores associated with the set of comments is generated, wherein each sentiment score indicates a type of sentiment expressed in a comment. The sentiment profile is generated responsive at least in part to the set of sentiment scores, the sentiment profile indicating the types of sentiment expressed in comments associated with the item of media content and stored in association with the item of media content.

Abstract: A set of comments associated with an item of media content is identified. A set of sentiment scores associated with the set of comments is generated, wherein each sentiment score indicates a type of sentiment expressed in a comment. The sentiment profile is generated responsive at least in part to the set of sentiment scores, the sentiment profile indicating the types of sentiment expressed in comments associated with the item of media content and stored in association with the item of media content.

Abstract: A set of comments associated with an item of media content is identified. A set of sentiment scores associated with the set of comments is generated, wherein each sentiment score indicates a type of sentiment expressed in a comment. The sentiment profile is generated responsive at least in part to the set of sentiment scores, the sentiment profile indicating the types of sentiment expressed in comments associated with the item of media content and stored in association with the item of media content.

Abstract: A set of comments associated with an item of media content is identified. A set of sentiment scores associated with the set of comments is generated, wherein each sentiment score indicates a type of sentiment expressed in a comment. The sentiment profile is generated responsive at least in part to the set of sentiment scores, the sentiment profile indicating the types of sentiment expressed in comments associated with the item of media content and stored in association with the item of media content.

Abstract: A set of comments associated with an item of media content is identified. A set of sentiment scores associated with the set of comments is generated, wherein each sentiment score indicates a type of sentiment expressed in a comment. The sentiment profile is generated responsive at least in part to the set of sentiment scores, the sentiment profile indicating the types of sentiment expressed in comments associated with the item of media content and stored in association with the item of media content.

Abstract: A set of comments associated with an item of media content is identified. A set of sentiment scores associated with the set of comments is generated, wherein each sentiment score indicates a type of sentiment expressed in a comment. The sentiment profile is generated responsive at least in part to the set of sentiment scores, the sentiment profile indicating the types of sentiment expressed in comments associated with the item of media content and stored in association with the item of media content.

Abstract: A set of comments associated with an item of media content is identified. A set of sentiment scores associated with the set of comments is generated, wherein each sentiment score indicates a type of sentiment expressed in a comment. The sentiment profile is generated responsive at least in part to the set of sentiment scores, the sentiment profile indicating the types of sentiment expressed in comments associated with the item of media content and stored in association with the item of media content.

Abstract: A system, method, and computer program product are provided for segmenting videos in a video hosting service. To determine segments related to timestamps, user generated comments are parsed for timestamps. The timestamps are organized into sets and a segment is identified for each set of timestamps. A start point for each segment is determined based on the timestamps in each set. The segment will have an endpoint that can be either the end of the itself, or optionally can be determined based on the timestamps. Each timestamp is associated with a segment. One or more indicators represent each timestamp and each indicator is coupled to the segment associated with timestamp represented by the indicator. The segments are ranked according to popularity or the submission time of their related timestamp indicators. Responsive to receiving a client request for a video, the comments, indicators, and segments are loaded to the client.

Abstract: A set of comments associated with an item of media content is identified. A set of sentiment scores associated with the set of comments is generated, wherein each sentiment score indicates a type of sentiment expressed in a comment. The sentiment profile is generated responsive at least in part to the set of sentiment scores, the sentiment profile indicating the types of sentiment expressed in comments associated with the item of media content and stored in association with the item of media content.

Abstract: A system, method, and computer program product are provided for segmenting videos in a video hosting service. To determine segments related to timestamps, user generated comments are parsed for timestamps. The timestamps are organized into sets and a segment is identified for each set of timestamps. A start point for each segment is determined based on the timestamps in each set. The segment will have an endpoint that can be either the end of the itself, or optionally can be determined based on the timestamps. Each timestamp is associated with a segment. One or more indicators represent each timestamp and each indicator is coupled to the segment associated with timestamp represented by the indicator. The segments are ranked according to popularity or the submission time of their related timestamp indicators. Responsive to receiving a client request for a video, the comments, indicators, and segments are loaded to the client.

Abstract: A system, method, and computer program product are provided for segmenting videos in a video hosting service. To determine segments related to timestamps, user generated comments are parsed for timestamps. The timestamps are organized into sets and a segment is identified for each set of timestamps. A start point for each segment is determined based on the timestamps in each set. The segment will have an endpoint that can be either the end of the itself, or optionally can be determined based on the timestamps. Each timestamp is associated with a segment. One or more indicators represent each timestamp and each indicator is coupled to the segment associated with timestamp represented by the indicator. The segments are ranked according to popularity or the submission time of their related timestamp indicators. Responsive to receiving a client request for a video, the comments, indicators, and segments are loaded to the client.

Abstract: A system, method, and computer program product are provided for segmenting videos in a video hosting service. To determine segments related to timestamps, user generated comments are parsed for timestamps. The timestamps are organized into sets and a segment is identified for each set of timestamps. A start point for each segment is determined based on the timestamps in each set. The segment will have an endpoint that can be either the end of the itself, or optionally can be determined based on the timestamps. Each timestamp is associated with a segment. One or more indicators represent each timestamp and each indicator is coupled to the segment associated with timestamp represented by the indicator. The segments are ranked according to popularity or the submission time of their related timestamp indicators. Responsive to receiving a client request for a video, the comments, indicators, and segments are loaded to the client.