Sentiment analysis in marketing

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In marketing, sentiment analysis predominantly falls under the realm of social media monitoring. Here the term “social media” doesn’t refer only to Facebook, Twitter and the others, but also to YouTube, product and service reviews in shops and portals, as well as forum posts. Today, opinions are very quickly formed and spread through these channels. So those who know how their own products are being talked about in comments and posts can react accordingly. Sentiment analysis isn’t limited to just texts though. Videos, images and even podcasts are closely examined as well.

Purpose of sentiment analysis
Sentiment analysis ascertains the mood on social media pertaining to products, service activities, campaigns and companies. Where opinions are predominantly negative, the company can analyze the reasons and react.

Example:
A service hotline is being badmouthed. The cause of this needs to be discovered. Is it due to a lack of service staff friendliness or competence? Are the waiting times to long? Is only insufficient assistance being offered? The cause analysis shows that the wait times are too long. To remedy this, more service personnel can be assigned to the project. In addition, other methods for quickly getting in touch, like for example a contact form or chats, can be offered and/or more prominently advertised. Afterwards, further sentiment analysis gives an indication of whether these measures have worked. If that is the case, satisfaction with the hotline will increase.

Sentiment analysis is especially important for marketing campaigns which predominantly focus on social networks. Are the viral videos being liked? Is the podcast well-received? Does the community like the Facebook posts? If the mood is negative, the campaign can be corrected.

How does sentiment analysis work?
In general, we differentiate between manual and automated analysis.

Manual analysis
Here, people undertake the inspection of the relevant data. They rate the opinions expressed on social media in regards to positive, negative or neutral tonality.

Automated analysis
With automated analysis, software searches through all available data and performs a classification. In doing so, mainly procedures based on linguistic sources are used or the concept of machine learning is applied.

Linguistic sources:
Here, the data is assessed on the basis of pre-determined positive and negative signal words.

The employees were friendly and competent, but the wait time was awful.

The words “friendly” and competent” are evaluated a canada cell phone number database s positive signal words, and the word “awful” as negative. Overall, the sentence is assessed as positive by the software, because two positive signal words are opposed by only one negative.

Problems with this method are the ambiguity of many words, slang terms, and that it doesn’t understand irony and context. For example a long battery service life is something positive, but assigning “long” a positive signal word status is difficult. In the context of wait time, it is of course negative.

Machine learning:
Here, software is taught via sample data whether a positive or negative statement is indicated. Once it has this knowledge, it can analyze unfamiliar text and determine its tonality. The sample data is critical with this method. If the software was trained with the aid of customer reviews about cars, then it will fail when evaluating film critiques.

Naturally, even the best software can’t understand all the nuances of human speech and classify the tonality correctly. For this people are still necessary.