Use Sentiment analysis Sentiment analysis is extremely useful in social media mo
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Question
Use Sentiment analysis
Sentiment analysis is extremely useful in social media monitoring as it allows us to gain an overview of the wider public opinion behind certain topics.The applications of sentiment analysis are broad and powerful. The ability to extract insights from social data is a practice that is being widely adopted by organisations across the world.
Impact of the success of Sentiment Analysis Tools on human society
Sentiment analysis applications have spread to many domains in the our life: from consumer products, healthcare and financial services to political elections and social events. A common task in opinion mining is to classify an opinionated document into a positive or negative opinion. In this paper, a study of different methodologies is conducted to rank polarity as to better know how the ironic messages affect sentiment analysis tools
How Text mining tools can be used for Sentiment Analysis
-Develop markting results by understand customer opinions.
-understand the emotions across social media
Sentiment analysis is the measurement of positive and negative language. It is a way to evaluate written or spoken language to determine if the expression is favorable, unfavorable, or neutral, and to what degree.
sensitivity analysis is a technique used to determine how different values of an independent variable impact a particular dependent variable under a given set of assumptions.
Tools used for Text mining and Sentiment Analysis
IBM SPSS Modler
SAS Enterprise Miner
Statistical Data Miner
ClearForest
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Explanation / Answer
Use Sentiment Analysis :
Sentiment analysis is also very useful in conducting online surveys. For Example, company wants to know how their product is working in the market so they try to conduct a product survey . Online approaches enable direct contact between the consumers and organization. The ability to extract insights from data helps organization to measure positives and negatives on the public reviews of their product .
Impact of the success of Sentiment Analysis Tools on human society:
The application of sentiment analysis goes far beyond marketing. Financial analysts use the sentiment analysis of Twitter and other social media websites to understand the public's thinking about the company in order to predict global market behavior. Social media data mining allows researchers to more consciously and spontaneously determine what people are actually talking about. In addition, rather than inferring from a small sample size, the ease of analyzing large amounts of data often allows you to analyze the entire population or approach it. general people get to know other users opinion before buying actual products.
How Text mining Tools can be used for sentiment analysis:
Text mining is required if useful information is needed Extract only important information from a large amount of text. But where do you start? What are the Popular tools, which technologies to use, and what are the features. In sentiment analysis, text mining tools are used to extract only required data on which sentiments needs to be calculated some test mining tools provide programming environment where logic of how to calculate the sentiments from the text, but some of the tools provide template system in this case a non-technical users can also measure the sentiments from the text.
Sentiment analysis:
Sentiment analysis is a type of data mining that measures people's opinions through natural language processing (NLP), computational linguistics, and text analysis. These data mining are used to extract and analyze subjective information from the network - mainly social media and similar. source. The analyzed data quantify the general public's perception or reaction to a particular product, person or idea, and reveals the background polarity of the information.it is also known as opinion mining.
Sensitivity analysis:
Sensitivity analysis is a data-driven investigation of how certain variables affect a single independent variable, and how these variables change will change the independent variable. Imagine you own your own business and provide a case for your smartphone. Each month, if you have too many cases, they will sit down and if they introduce new phones, they may even be wasted. However, if you do not provide enough cases, you cannot sell as much as possible, so you do not maximize your profits. Therefore, at the beginning of each month, you are faced with how many cases you decide to make.
Tools used for Text mining and Sentiment analysis:
The choice of tools depends on the specific issues you deal with in sentiment analysis. Some of the tools and when preferably use which one are mentioned.
WEKA - If you already have data with feature vectors for each data point, you can use Weka to cluster the data. If you have the golden forecast output of data, you can build a classifier. Highly configurable and easy-to-use GUI available.
NLTK - If you know Python programming, then NLTK is a wise choice because it contains both of these features. In addition, you can easily use vocabulary resources, such as WordNet often needed in sentiment analysis.
GATE- This is useful if you want to develop a pipeline. Language analysis modules for various languages ??are provided by developers and can be used to insert pipelines. If you have a new method, you can write a custom module in JAVA and insert the pipe, a complete system will be available.
StanfordCoreNLP - If you need word classification, syntactic analysis (phrase structure or dependency analysis), common references or named entities in the text. These have been used as potential features by the emotional analysis research community.
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