Sentiment Analysis

Sentiment analysis, also known as opinion mining, is the interpretation and classification of emotions (positive, negative, and neutral) through the text of the review rather than the rating given by the reviewer. This allows businesses to better identify the sentiment of products, brands, and/or services in online conversations or feedback received on the product.

Most classification algorithms focus on the prediction of nominal class data labels. Pattern recognition helps in predicting categories or labels on an ordinal scale, known as ordinal classification or ordinal regression. Studies show using machine learning techniques can predict new results and achieve more accurate ratings.

Types of Sentiment Analysis

Fine-grained Sentiment Analysis

• Very Positive = 5 stars
• Positive = 4 stars
• Neutral = 3 stars
• Negative = 2 stars
• Very negative = 1 stars

Emotion Detection

Aims at detecting emotions:

• Happiness
• Frustration
• Anger
• Sadness
• So on..

Aspect-based Sentiment Analysis

Helps to identify aspects of the review that are either shed in a negative light or positive. Example would be "The battery life of this camera is too short." An aspect-based qualifier would be able to determine the sentence expresses a negative opinion about the battery.

Multilingual Sentiment Analysis

Sentiment Analysis models built to detect and produce analytics no matter the language.

Challenges with Sentiment Analysis

The human language is complex as it is and combine it with the complexity of machine learning is never easy. The detection of subjective and objective texts is as important as knowing the tone of the reviewer. "The package is nice" and "the package is red" are two basic review sentiments. Whereas the first review may be treated as positive and the second may be treated as neutral. "Nice" is treated more subjective than that of "red." Other difficulties is understand context. One problem that arise from context is changes in polarity. Responses such as "Everything of it" and "Absolutely nothing!" can have very different sentiment analysis depending on the question asked.

Irony and sarcasm can be missed all together through everyday human interaction. Now try getting a machine to understand the difference. Another challenge is when comparisons enter into a review and the entrance of emojis to express feelings. Emojis have their own unicode characters that can be utilized when preprocessing reviews.