Context Based Sentiment Analysis of Product Review Videos by Users

The word “Social” has taken a whole new meaning in today’s digital era. Simply going out to enjoy is no longer the only “social” criteria. Social now is — giving a peek in your personal and professional life to your connections. Facebook, Twitter, Instagram, and other leading platforms have connected people in ways that were unimaginable 20 years ago. More than that, these platforms have become an excellent resource for businesses to reach new customers, understand the perception of their brand, seek feedback, and improve customer experience. Today, analytics has become a driving force for businesses, enabling them to use the power of data to grow user base and revenues.

What exactly is Sentiment Analysis?

One such power, Sentiment Analysis — a concept popular amongst Machine Learning enthusiasts — is leveraged by companies to understand customer sentiment or emotion regarding the company’s products. Such an analysis crawls social media platforms to gather data on user sentiments on specific products by the company. It analyzes each user’s comment to classify it as positive, negative, or neutral, and to provide the overall result. The market has witnessed an exponential growth since 2016.

Customer Experience & Product/Market Research categories will continue to see the highest growth rates

Where the traditional approach falls apart!!

“Without context a piece of information is just a dot. It floats in your brain with a lot of other dots and doesn’t mean a damn thing. Knowledge is information-in-context…connecting the dots” — Michael Ventura

I see Sentiment Analysis as a powerful technique that is yet to be fully tap-into. The current textual analysis has many shortcomings. “Yeah, no one does it better than you!”. The software will classify it as a positive sentiment. But what if it was Sarcasm? Here is another one. “…Amazon always does it”. What should I make of this comment? Let’s take a step back in the text and see a sentence before this. “Amazon is great in delivering products on time. Amazon always does it” — makes the latter sentence positive. However, “Amazon just delivered a damaged piece. Amazon always does it”- makes the text negative. Context is very crucial in understanding a sentiment. Text analysis has been missing the context of the conversation!

#product-design #machine-learning #data analysis

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Context Based Sentiment Analysis of Product Review Videos by Users

The word “Social” has taken a whole new meaning in today’s digital era. Simply going out to enjoy is no longer the only “social” criteria. Social now is — giving a peek in your personal and professional life to your connections. Facebook, Twitter, Instagram, and other leading platforms have connected people in ways that were unimaginable 20 years ago. More than that, these platforms have become an excellent resource for businesses to reach new customers, understand the perception of their brand, seek feedback, and improve customer experience. Today, analytics has become a driving force for businesses, enabling them to use the power of data to grow user base and revenues.

What exactly is Sentiment Analysis?

One such power, Sentiment Analysis — a concept popular amongst Machine Learning enthusiasts — is leveraged by companies to understand customer sentiment or emotion regarding the company’s products. Such an analysis crawls social media platforms to gather data on user sentiments on specific products by the company. It analyzes each user’s comment to classify it as positive, negative, or neutral, and to provide the overall result. The market has witnessed an exponential growth since 2016.

Customer Experience & Product/Market Research categories will continue to see the highest growth rates

Where the traditional approach falls apart!!

“Without context a piece of information is just a dot. It floats in your brain with a lot of other dots and doesn’t mean a damn thing. Knowledge is information-in-context…connecting the dots” — Michael Ventura

I see Sentiment Analysis as a powerful technique that is yet to be fully tap-into. The current textual analysis has many shortcomings. “Yeah, no one does it better than you!”. The software will classify it as a positive sentiment. But what if it was Sarcasm? Here is another one. “…Amazon always does it”. What should I make of this comment? Let’s take a step back in the text and see a sentence before this. “Amazon is great in delivering products on time. Amazon always does it” — makes the latter sentence positive. However, “Amazon just delivered a damaged piece. Amazon always does it”- makes the text negative. Context is very crucial in understanding a sentiment. Text analysis has been missing the context of the conversation!

#product-design #machine-learning #data analysis

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

• J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7, Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

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import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

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for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

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TokenInfo(type=62 (ENCODING),  string='utf-8')

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TokenInfo(type=1  (NAME),      string='color')

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TokenInfo(type=54 (OP),        string='=')

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TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

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TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

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TokenInfo(type=4  (NEWLINE),   string='')

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TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer