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Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.
Multimodal sentiment analysis is a technology for traditional text-based sentiment analysis, which includes modalities such as audio and visual data. [1] It can be bimodal, which includes different combinations of two modalities, or trimodal, which incorporates three modalities. [ 2 ]
In other words, data analysis is the phase that takes filtered data as input and transforms that into information of value to the analysts. Many different types of analysis can be performed with social media data, including analysis of posts, sentiment, sentiment drivers, geography, demographics, etc. The data analysis step begins once we know ...
Topsy Labs was a social search and analytics company based in San Francisco, California. [1] [2] The company was a certified Twitter partner and maintained a comprehensive index of tweets, numbering in the hundreds of billions, dating back to Twitter's inception in 2006.
When talking about social data analytics, there are a number of factors it's important to keep in mind (which we noted earlier): [1] Sophisticated Data Analysis: what distinguishes social data analytics from sentiment analysis is the depth of the analysis. Social data analysis takes into consideration a number of factors (context, content ...
TipTop Technologies is a real-time web and social search engine with a platform for semantic analysis of natural language.Tip-Top Search provides results capturing individual and group sentiment, opinions, and experiences there from the content of various sorts such as real-time messages from Twitter or consumer product reviews on Amazon.com. [1]
Sentiment analysis 2009 [47] [48] A. Go et al. ASU Twitter Dataset Twitter network data, not actual tweets. Shows connections between a large number of users. None. 11,316,811 users, 85,331,846 connections Text Clustering, graph analysis 2009 [49] [50] R. Zafarani et al. SNAP Social Circles: Twitter Database Large Twitter network data.
Relate unstructured text with structured data such as dates, numbers or categorical data for identifying temporal trends or differences between subgroups or for assessing relationship with ratings or other kind of categorical or numerical data. Visualization tools to visualize and interpret text analysis results: Dendrogram with optional bar chart