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Sentiment Analysis

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Sentiment analysis is contextual mining of text which identifies and extracts subjective information in source material, and helping a business to understand the social sentiment of their brand, product or service while monitoring online conversations. Creative use of advanced artificial intelligence techniques can be an effective tool for doing in-depth research. Sentiment Analysis It is the most common text classification tool that analyses an incoming message and tells whether the underlying sentiment is positive, negative our neutral Intent Analysis Intent analysis steps up the game by analyzing the user’s intention behind a message and identifying whether it relates an opinion, news, marketing, complaint, suggestion, appreciation or query An example of Sentiment analysis : Here's a case study on public data of Facebook comments: Breakdown of Sentiment for Categories Noticeably, comments related to all the categories have a negative sentiment majorly, bar one. The number of pos...
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Applications of NLP in Social Media

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NLP in social media is particularly useful to  analyze huge amounts of text data  in a fast and cost-effective way – from internal documents, communications with customers, or all over the web.  Imagine you need to examine a sizable collection of evaluations to determine what consumers are saying about your offering.  Topic labelling and sentiment analysis can be used together to identify the most popular features or characteristics of your product as well as to gauge how people feel about them (are their comments favorable, negative, or neutral?). Aspect-based sentiment analysis is the term used to describe this method. In addition to brand monitoring, topic analysis has many other applications in business intelligence, sales and marketing, SEO, product analytics, and knowledge management. Some important use cases are: Trending Topic Detection Information Retrieval and Extraction Information Summarization Sentiment Detection Rumor detection Adult Content Filtering...
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Linguistic Pre-Processing of Social Media Texts

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There are several existing linguistic pre-processing tools such as tokenizers, part-of-speech taggers, parsers, and named entity recognizers, with a focus on their adaptation to social media data. Following steps are carried out during text preprocessing: Tokenization is the process of breaking down a text into words and sentences. We delete the punctuation and lowercase the words. A letter from the end of each word is removed until the stem is reached in the rudimentary procedure known as stemming. There are numerous exceptions in languages like English. Lemmatization looks beyond word reduction and uses a language's whole vocabulary to apply a morphological analysis to words. For instance, third-person nouns are transformed to first-person, while past and future tense verbs are changed to present tense. Stemming is the process of reducing words to their root form. Normalization is a process that converts a list of words to a more uniform sequence. Other operations can interact wi...
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Data Collection

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There are various different complementary aspects of social media text analysis. The results of the information analysis could be influenced by the quality of collected input data. In order to use empirical methods of natural language processing or statistical machine learning algorithms, we need to build or acquire data for training or development, and for testing. These data sets need to be annotated. At least the test data needs to be annotated, so that we can evaluate the algorithms. The training data needs to be annotated in case the algorithms are supervised learning algorithms, while unsupervised learning algorithms can use the data as it is, without additional annotations (though they could benefit from a small annotated development data set). And while doing this, spam in the dataset must be avoided.  Social data collection depends on the intended task and application. Textual data from social media can be collected in various forms such as microblog messages, image descri...
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Introduction to Social Media Analysis

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Social media is a phenomenon that has recently expanded throughout the world and quickly attracted billions of users. This form of electronic communication through social networking platforms allows users to generate its content and share it in various forms of information, personal words, pictures, audio, and videos. Therefore, social computing is formed as an emerging area of research and development that includes a wide range of topics such as web semantics, artificial intelligence, natural language processing, network analysis, and big data analytics. Over the past few years, online social networking sites (Facebook, Twitter, YouTube, Flickr, MySpace, LinkedIn, Metacafe, Vimeo, etc.) have revolutionized the way we communicate with individuals, groups, and communities, and have altered everyday practices. One only needs to look at the recent statistics to prove this fact.                                ...
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