Overview


In today’s internet world, there is lots of business information. But 80% of this information is unstructured i.e. unorganized, and it is expected to be 93% by 2020 due to constant use of social media, emails, documents, photos, videos, etc. This unstructured information gives lot of information about your customers, customer feedback about your brand, feedback about your competitors and much more. People write about their experience at different places, whether it was good or bad. They give reviews about different products they have used. This impacts the views of other people about this product. First thing people see before buying a product or service is online reviews.

We will see how Review Inspector analyzes sentiment of this reviews so that business owners can align their strategies with customer demand. Review Inspector does sentiment analysis as well as POS Tagging of reviews.


Sentiment Analysis

  • Sentiment Analysis gives whether the sentiment of the review is positive, negative or neutral.
  • You can input a sentence of your choice and gauge the underlying sentiment.

POS Tagging

  • POS Tagging (Part Of Speech Tagging) is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context.


Our Approach


To solve the problem, we use INTELLIHUB . Lets see how INTELLIHUB works:

  • First user will pass sentence.
  • It goes to Intellihub Language (a Natural Language Processing module in INTELLIHUB ).
  • It analyzes and gives sentiment i.e. Positive, negative or neutral as well as Part of Speech of all words using POS Tagger.

In Intellihub, we can build model:

  • Using SDK (For Developer)


Using SDK


If you want to develop a model using SDK, just copy API key from INTELLIHUB Console.




To install python SDK use following command:

pip install intellihub
                            



Connect To Intellihub

Description

You can access the services provided by enabling API for Intellihub Language. INTELLIHUB provides IntellihubClient where you have to pass your APP KEY as an argument.

Code


import intellihub

c = intellihub.IntellihubClient("YOUR API KEY")

 

import com.spotflock.IntellihubClient;

IntellihubClient c = new IntellihubClient("YOUR API KEY");

 

Sentiment Analysis

Description

The intellihub language's Sentiment Analysis APIs identifies the sentiments and returns the json with sentiment analysis details.

Code


sentence = client.sentiment_analysis("I really like the new design of your website")
 

Response


{
    "nltk_vader": {
        "emotion": "POSITIVE",
        "scores": {
            "compound": 0.4201,
            "negative": 0,
            "positive": 0.285,
            "neutral": 0.715
        }
    }
}
 

String response = c.sentimentAnalysis("I really like the new design of your website");
System.out.println(response);
 

Response


{
    "nltk_vader": {
        "emotion": "POSITIVE",
        "scores": {
            "compound": 0.4201,
            "negative": 0,
            "positive": 0.285,
            "neutral": 0.715
        }
    }
}
 

POS Tagger

Description

You can find Part Of Speech (POS) of each word in a sentence using Intellihub Language.

Code


sentence = client.pos_tagger("The old tired man was sitting under a tree and patiently waiting for his son to arrive")
 

response


{
    "spacy": {
        "result": {
            "The": "DT",
            "old": "JJ",
            "tired": "JJ",
            "man": "NN",
            "was": "VBD",
            "sitting": "VBG",
            "under": "IN",
            "a": "DT",
            "tree": "NN",
            "and": "CC",
            "patiently": "RB",
            "waiting": "VBG",
            "for": "IN",
            "his": "PRP$",
            "son": "NN",
            "to": "TO",
            "arrive": "VB"
        }
    }
}
 

r = c.posTagger("The old tired man was sitting under a tree and patiently waiting for his son to arrive");
System.out.print(r);
 

response


{
    "spacy": {
        "result": {
            "The": "DT",
            "old": "JJ",
            "tired": "JJ",
            "man": "NN",
            "was": "VBD",
            "sitting": "VBG",
            "under": "IN",
            "a": "DT",
            "tree": "NN",
            "and": "CC",
            "patiently": "RB",
            "waiting": "VBG",
            "for": "IN",
            "his": "PRP$",
            "son": "NN",
            "to": "TO",
            "arrive": "VB"
        }
    }
}
 

Summary


Review Inspector is just a small application of Intellihub Language. We can extend the use case such as:

  • Analysing sentiment of comments for our social media posts.
  • We can also use for faster customer support by filtering negative sentiments in forums by keeping them as high priority.