The Avenga Team
Sentiment analysis is one method to gather and process customer-supplied information, and then convert it to a quality customer experience. As it explodes onto the scene, what can technology actually do to enhance business efforts?
Here are some tips on how to implement various sentiment analysis techniques with ease and utmost efficiency.
Some platforms apply automated (machine-powered) sentiment. Others believe in human-powered sentiment analysis, while many use a hybrid system. Decide which is best for your business before launching the effort.
For example, advancements in natural language processing make machine-powered processing a perfect choice for enterprises operating at a large scale with the need for timely analysis of huge data volumes. Though with some limits, automated sentiment analysis is statistically accurate compared with human-powered analysis.
We experimented with more than 10 machine learning algorithms for gathering and processing online customer sentiments in order to find the most accurate and promising ones. Here you can find sentiment analysis overview and results.
Human-powered platforms perform better for small project-based data sets. Even in the era of artificial intelligence, human potential in research tasks is still irreplaceable. Platforms like Mechanical Turk or Canvs claim to understand language through nuance and intonation, but it is recognized that interpreting the whole complexity of emotions, sarcasm, double meaning and slang is mostly beyond the reach of the machine learning tools.
Field-oriented influencers spread their thoughts and ideas to a wide audience and specifically impact their field. There are many popular bloggers, social personalities, and columnists that are followed by huge audiences across the digital world, including Twitter, LinkedIn, Facebook, Instagram, YouTube, on personal blogs, etc. Their linguistic behavior can guide public opinion and their sentiment is especially valuable for further opinion evaluations for your dimension of interest. Consequently, you can sharpen your marketing efforts and focus on effective work with influencers, either by mitigating critics or intensifying positive feedback.
Domain-specific sentiment dictionaries are also helpful for efficient sentiment analysis. There is a large array of customized sentiment lexicon resources that could help increase the accuracy of your analysis. Some domains are better investigated than others, and some lack thorough studies. Before implementing an analysis, search for an appropriate sentiment dictionary or domain-specific lexicon lists to ease the process.
Customers’ negative sentiment can provide an even more complete picture for your analysis. Evaluation of unfavourable opinions is extremely beneficial for enterprise development, as it gives you the opportunity to address controversy and make your business perform better without compromising efficiency.
70% of the most successful companies consider customer feedback to be of primary importance. Sentiment analysis helps business to deliver better customer experience by extracting the underlying meaning from the message. Where can it be used? The application area is immense:
Is marketing a prevalent application area for sentiment analysis? It is, but certainly not exclusive.
Driven by the increase of two-way communication, business is striving to improve the understanding of the potential customer. Analysts and computers aggregate and evaluate human reaction on social media, call-center feedback, and websites. In order to conform to the language evolution, and to re-categorise sentiment, sentiment analytics will continuously evolve.