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  2. Sentiment analysis - Wikipedia

    en.wikipedia.org/wiki/Sentiment_analysis

    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.

  3. Multimodal sentiment analysis - Wikipedia

    en.wikipedia.org/wiki/Multimodal_sentiment_analysis

    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 ]

  4. Social media analytics - Wikipedia

    en.wikipedia.org/wiki/Social_media_analytics

    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 ...

  5. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Twitter Dataset for Arabic Sentiment Analysis Arabic tweets. Samples hand-labeled as positive or negative. 2000 Text Classification 2014 [53] [54] N. Abdulla Buzz in Social Media Dataset Data from Twitter and Tom's Hardware. This dataset focuses on specific buzz topics being discussed on those sites.

  6. Kaggle - Wikipedia

    en.wikipedia.org/wiki/Kaggle

    Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.

  7. Text mining - Wikipedia

    en.wikipedia.org/wiki/Text_mining

    The term text analytics also describes that application of text analytics to respond to business problems, whether independently or in conjunction with query and analysis of fielded, numerical data. It is a truism that 80% of business-relevant information originates in unstructured form, primarily text. [ 9 ]

  8. Social network analysis - Wikipedia

    en.wikipedia.org/wiki/Social_network_analysis

    [24] [25] Even in the study of literature, network analysis has been applied by Anheier, Gerhards and Romo, [26] Wouter De Nooy, [27] and Burgert Senekal. [28] Indeed, social network analysis has found applications in various academic disciplines as well as practical contexts such as countering money laundering and terrorism. [citation needed]

  9. Cultural analytics - Wikipedia

    en.wikipedia.org/wiki/Cultural_analytics

    Cultural analytics refers to the use of computational, visualization, and big data methods for the exploration of contemporary and historical cultures. While digital humanities research has focused on text data, cultural analytics has a particular focus on massive cultural data sets of visual material – both digitized visual artifacts and contemporary visual and interactive media.