Five things you should know about sentiment analysis!
You don’t need be a technical expert to understand the principles of sentiment analysis, but it’s worthwhile giving the topic some thought. Practical uses and theoretical potential have demonstrated in recent years that sentiment analysis is a technology of the future, soon to become a necessity for entities that hope to remain competitive, especially as prices and technical requirements for the technology decrease.
So, as we move forward into the brave new world, here are five things you should know about sentiment analysis.
1. Sentiment analysis engines rely mostly on social media
There’s a lot of controversy and mixed feelings surrounding social media these days, but sentiment analysis is a unique way to make public social media posts useful. While some sentiment analysis engines draw on different sources as well, all rely on the written text posted by users on social media including blogs, forums and reviews.
Sentiment analysis AI scans all this user-created content online and classifies each individual text in a binary (positive or negative) or multi-class fashion. The final analysis is effectively the sum of this classification applies to countless numbers of posts online, giving you an accurate picture of how the public feels about a certain issue or product.
2. Data sets and pre-processing
Before analysis and classification of text even begins, data must be gathered and go through an initial processing phase that helps AI tools make better sense of what’s being said and whether its positive or negative. Lots of datasets are already in existence, particularly for Twitter and Amazon product reviews, cutting back on the amount of data gathering that sentiment analysis engines need to performs themselves.
During pre-processing, numbers are often removed from the text along with punctuation, everything is made lowercase and stopwords are cut out.
3. Classification
There are three main methods an AI platform can use to determine whether a text expresses positive or negative sentiment. The first, machine learning, helps construct a classifier that can identify the sentiment of a text on its own and improve its accuracy overtime. The second, lexicon-based classification, relies on a pre-programmed set of words and phrases to determine the nature of the text in question.
The third involves a healthy mix of the first two and seems to offer the best results. A pre-programmed database of words and phrases, each assigned with a positive or negative value, will struggle to understand the nuanced meanings of certain combinations of words or sarcastic phrases. But an AI that feeds on that same set of rules and gradually improves upon them can only get better and better.
4. Evaluation
And how can you or the AI evaluate the efficacy of its work? What are the benchmarks on which sentiment analysis is judged? The answer to that is precision, recall, F-score and accuracy. For mutli-class classifiers, macro, micro and weighted F1-scores can also be useful.
5. Visualization
The final step in sentiment analysis is to produce the results in a visually meaningful way so that us humans can understand what’s going on. Graphs, histograms, confusion matrices, wordclouds, interactive maps and sparkline-style plots are all good options depending on the topic under consideration.
But there’s a secret sixth thing you should know about sentiment analysis: it’s almost been completely inaccessible to small entities and individuals. Until now. The high financial and computational costs of running a sentiment analysis engine have kept the technology in the hands of a few, but Senno, a new blockchain-based venture, is drastically reducing resource requirements by offering a decentralized sentiment analysis engine that utilizes the computing power of countless contributors. Yes, this technology is finally reaching maturity, where everyone has the ability to benefit from its potential.
