The analysis of texts to determine the writers' or speakers' opinion and attitude expressed, and how the results can be used.

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sentiment: a point of view, opinion, feeling, or attitude held or expressed by a person

What is sentiment analysis?

Sentiment analysis is also known as opinion mining.

In its simplest form, it’s a way of determining how positive or negative the content of a text document is, based on the relative numbers of words it contains that are classified as either positive or negative.

Positive words would include words such as 'amazing', 'friendly', 'clean', 'exceeded', and 'prompt'.

Negative words could be words like 'scam', 'unprofessional', 'rude', 'refund', and 'incompetent'.

Sentiment analysis can identify the attitude or opinions of a writer or speaker with regard to a particular topic, and whether that attitude is negative, positive, or neutral.

It can also reveal their emotional state, and the intended effect of their words.

The benefits of sentiment analysis

Sentiment analysis is a useful tool for any organization or group for which public sentiment or attitude towards them is important for their success - whichever way that success is defined.

On social media, blogs, and online forums millions of people are busily discussing and reviewing businesses, companies, and organizations. And those opinions are being ‘listened to’ and analysed.

Those being discussed are making use of this enormous amount of data by using computer programs that don’t just locate all mentions of their products, services, or business, but also determine the emotions and attitudes behind the words being used.

The results from sentiment analysis help businesses understand the conversations and discussions taking place about them, and helps them react and take action accordingly.

They can quickly identify any negative sentiments being expressed, and turn poor customer experiences into very good ones.

They can create better products and services, and they can formulate the marketing messages they send out according to the sentiments being expressed by their target audience or customers.

All of which adds up to increased sales and revenue.

By listening to and analysing comments on Facebook and Twitter, local government departments can gauge public sentiment towards their department and the services they provide, and use the results to improve services such as parking and leisure facilities, local policing, and the condition of roads.

Universities can use sentiment analysis to analyze student feedback and comments garnered either from their own surveys, or from online sources such as social media. They can then use the results to identify and address any areas of student dissatisfaction, as well as identify and build on those areas where students are expressing positive sentiments.

And by analysing the sentiment behind customer reviews on sites like TripAdvisor and Yelp, hotels and restaurants can not only manage their reputations by improving the services offered, but can also gauge the general customer attitude to their business or brand.

Businesses can compare their results with those of their competitors to better understand people’s attitude to their business. They can identify where they may be excelling, or identify where there’s room for improvement compared to the competition.

They can also conduct market research into general sentiment around key issues, topics, products, and services, before developing and launching their own new services, products or features.

Limitations of automated sentiment analysis

Sentiment analysis tools can identify and analyse many pieces of text automatically and quickly.

But computer programs have problems recognizing things like sarcasm and irony, negations, jokes, and exaggerations - the sorts of things a person would have little trouble identifying. And failing to recognize these can skew the results.

'Disappointed' may be classified as a negative word for the purposes of sentiment analysis, but within the phrase “I wasn't disappointed", it should be classified as positive.

We would find it easy to recognize as sarcasm the statement "I'm really loving the enormous pool at my hotel!", if this statement is accompanied by a photo of a tiny swimming pool; whereas an automated sentiment analysis tool probably would not, and would most likely classify it as an example of positive sentiment.

With short sentences and pieces of text, for example like those you find on Twitter especially, and sometimes on Facebook, there might not be enough context for a reliable sentiment analysis. However, in general, Twitter has a reputation for being a good source of information for sentiment analysis, and with the new increased word count for tweets it's likely it will become even more useful.

So, automated sentiment analysis tools do a really great job of analysing text for opinion and attitude, but they're not perfect.

When you're using a tool like Typely to analyse your text to see if it conveys the sentiment you want for your readers/audience, combine the results it gives you with your human judgement to identify anything the tool may not be able to easily determine.

Typely highlights phrases in your text by positive and negative sentiment, making it super easy for you to see where your document is either expressing exactly the sentiments you want it to, or where you may need to make some changes.