Sentiment analysis is a technique used to process textual data. It has a diverse range of real-life applications. Sentiment analysis relies on an Artificial Intelligence engine powered by Machine Learning and Natural Language Processing. Some of these include:
social media monitoring
product management and analysis
customer support and feedback analysis
brand monitoring and reputation management
Contextual mining of text identifies and extracts subjective information in source material which contributes to understanding the social sentiment of a brand, product or service while monitoring online conversations. There are cases where social media streams analysis limited to just basic sentiment analysis and count based metrics. This may result in missing crucial insight.
However, recent advances in deep learning have significantly improved the ability of algorithms to analyze text.
There are steps involved in the entirety of the process. They include:
- Data gathering
This involves data that will be later analyzed. Some sources of this data include social media, majorly Twitter, using data scraping tools, APIs, customers’ data feed, or even gather data from user reviews on services like Google and Yelp.
- Text cleaning
Text cleaning tools will allow us to process the data and prepare it for analysis by:
-> Removal of stopwords - Prepositions and conjunctions mostly
(a, and, or, but, how, what…).
-> Dropping punctuation marks (commas, periods…).
-> Stemming - Reduction of a word to its root. For example
running, runner and run share the stem run
- Analyzing the data
At this point, sentiment analysis algorithms can be used to analyze the gathered data. The most common classification is the spectrum between “positive” and “negative.” However, more refined tools may also identify more complex sentiments such as anger, sadness, etc.
The algorithms will different sentiment libraries to identify opinions and classify them.
- Understanding the results
Last but not least, we should be able to see the data grouped into major categories. This in turn clarifies the reactions as positive, neutral, or negative reactions.
Other attributes such as tagging each sentiment with its original date are particularly important. A timeline will show us if we had surges of positive sentiments or troughs of negative sentiments at specific moments in time.
This might therefore give correlations between something that happened on a specific date and a surge of opinions regarding the brand in question.
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