Incorporating Sentiment Analysis into E-commerce
Studying users’ behavior and understanding their sentiments have become substantial to businesses with the increasing platforms operating in the digital space and users’ traffic rapidly growing on these online platforms. The Internet replaced sources such as friends, relatives, and consumer reports which used to be the primary sources of opinions on products and services. As a method of determining users’ opinions and subjectivity, sentiment analysis is an emerging new source.
An item’s online reputation is simply the opinion of its internet community — buyers and users. Since psychological scrutiny of buyers’ sentiments about a specific product can drive motivation in prospective buyers in order to influence their buying decision, analyzing their sentiments can be critical to determining an e-commerce business’ future trajectory.
Buyers’ Sentiments are Crucial
When a buyer decides to make a purchase from an online store, he prefers to have a peek into other buyers’ views about the item, just to be sure that the product he is spending his pretty penny on is worth grabbing or not. The sentiments of buyers about a product, whether negative or positive, revealed in the ratings and reviews give prospective buyers a sense of acumen to decide to buy the same product. It, thus, becomes of utmost importance for an online platform to incorporate sentiment analysis into e-commerce practice.
What Sentiment Analysis Can Reveal
People’s sentiments about a product can be revealed by sentiment analysis. As a result, sentiment analysis can first be used to provide indications and recommendations on what products to choose based on the wisdom of the mass. Generally, certain aspects of a product appeal to you when choosing it to buy. Ratings based on global averages could be misleading. Based on sentiment analysis, certain aspects of the product can be rated based on the opinions of the reviewers.
Another utility of sentiment analysis is for companies that want customers’ opinions on their products. They can then improve the aspects that the customers found unsatisfying. Sentiment analysis can also determine which aspects are more important for the customers.
It is possible to conduct sentiment analysis at three different levels: document level, sentence level, and aspect/feature level, which is then followed by aspect extraction and sentiment classification.
Document Level Classification
The overall sentiment of the opinion holder is analyzed in this process, and a whole opinion is classified according to it. In order to classify a review, one must determine if it is positive, negative, or neutral.
“Although a little large, this phone is so nice. I like the touch screen. The voice quality is clear. I just love it!”
Is the review positive or negative? A document-level classification is most effective when it is written by a single person and expresses a single opinion or sentiment.
Sentence Level Classification
The sentence level classification includes two classifications based on subjectivity and sentiments:
I. Subjectivity classification leads to be objective and subjective nature
II. Sentiment classification tends to be of positive and negative nature
Subjective sentences convey feelings, emotions, and beliefs, whereas objective sentences present facts. Subjective sentences can be identified in various ways, such as using Naïve Bayesian classification. In order to understand what a user’s statement refers to, we need to know if it is positive or negative. The sole objective of it is to filter out sentences with no opinion and to determine to a certain extent whether sentiments are positive or negative about entities, products, or services. A subjective sentence may include multiple opinions and factual and subjective clauses.
Aspect/Feature Level Classification
This process aims to determine whether the opinion holder’s comment on an object feature is positive, negative, or neutral by identifying and extracting its features. We group features based on their synonyms and create a feature-based summary based on multiple reviews.
It is necessary to find all adjectives that frequently occur across reviews in order to identify all the aspect terms (e.g., great food) that appear in a sentence. This can be followed by building a list of phrases that occur frequently. Alternatively, you can search the reviews for all aspects. For instance, a restaurant’s food, service, value, and décor could be its aspects.
Positive and negative sentiments, strong or weak, are expressed through words. When performing sentiment analysis, it is important to distinguish positive and negative sentiments. Sentiment lexicons can be used for this purpose. A sentiment lexicon categorizes words according to their positive or negative sentiment. The words with negative connotations used in a sentence in customer reviews can be considered sentiments.
How Sentiment Analysis can Help Businesses Gain Accurate Consumer Insight
Organizations use different channels, e.g., social media, online forums, surveys, and online opinion polls, to get an insight into the feedback they receive from buyers for their products and services. However, the computational study of how a product or service is doing in the market and how customers are responding to it in terms of purchase can bring a huge margin of profit to a business. Sentiment analysis can significantly help an e-commerce business set the right path for product or service improvement based on consumer feedback.
Sentiment analysis has evolved with the evolution of the internet, particularly with the emergence of e-commerce platforms. Since sentiment analysis tasks are challenging due to the complexity of the sentiment analysis datasets sourcing and their natural language processing (NLP) origin, expert intervention is necessary. Cogito, an NLP expert, has been in the industry for over a decade and can bring you experts to perform sentiment analysis around your e-commerce businesses to derive accurate customer insight for products and service offerings.
The growing need for product insights – and the technical challenges currently facing the field will keep sentiment analysis and opinion mining relevant for the foreseeable future in the e-commerce industry.
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