Sentiment analysis (opinion mining)

Sentiment analysis, which is also called opinion mining, involves building a system to collect and examine opinions about some object in blog posts, comments, reviews or tweets.

This challenge will have to be watered down a bit so that people will not be overwhelmed by writing and reviewing code. Therefore, the "Collecting" of the opinions can just be strings that are input into the program.

Entry-level analysis:

The basic idea of this challenge is to place "weights" on words, and you keep track of all the "weight" on a "scale". This scale will be a percentage, with 0% being a very placid input string, and 100% being a very irate input string.

Certain words with a negative connotation will add to this scale to varying degrees depending on the word (stupid = +5, hate = +10, dumb = +7, etc.); words with a positive connotation will subtract from this scale to varying degrees depending on the word (nice = -3, awesome = -5, sweet = -6, cool = -1, etc.).

The only challenge for the entry-level analysis is to print out the sentiment value for an input string.

Advanced-level analysis:

The idea is the same as the entry-level analysis, but instead of finding the overall sentiment value for an input string, you have to find the sentiment value for certain objects. An example:

I really hate ___________________, it is better to spend my money on something I love like _________________.

You will have to attribute the sentiment value to the object in each of those spaces; the first object would have a very "heated" sentiment value, while the second object would have a very "calm" sentiment value.

Sentiment analysis (opinion mining)

Sentiment analysis, which is also called opinion mining, involves building a system to collect and examine opinions about some object in blog posts, comments, reviews or tweets.

This challenge will have to be watered down a bit so that people will not be overwhelmed by writing and reviewing code. Therefore, the "collecting" of the opinions can just be strings that are input into the program.

Simply put: Your job is to "weigh" the value of words from an input string on a scale based on their connotation.

This challenge can be as simple as you want (just finding the sentiment value for the whole string). It can also be as complex as you want (finding the sentiment value for certain, specific words, collecting data from social media sites, etc.).

Sentiment analysis (opinion mining)

Sentiment analysis, which is also called opinion mining, involves building a system to collect and examine opinions about some object in blog posts, comments, reviews or tweets.

This challenge will have to be watered down a bit to more of a entry level. This is so that people will not be overwhelmed by writing and reviewing code. Therefore, the "collecting" of the opinions can just be strings that are input into the program.

Sentiment analysis (opinion mining)

Sentiment analysis, which is also called opinion mining, involves building a system to collect and examine opinions about some object in blog posts, comments, reviews or tweets.

This challenge will have to be watered down a bit so that people will not be overwhelmed by writing and reviewing code. Therefore, the "Collecting" of the opinions can just be strings that are input into the program.

Entry-level analysis:

The basic idea of this challenge is to place "weights" on words, and you keep track of all the "weight" on a "scale". This scale will be a percentage, with 0% being a very placid input string, and 100% being a very irate input string.

Certain words with a negative connotation will add to this scale to varying degrees depending on the word (stupid = +5, hate = +10, dumb = +7, etc.); words with a positive connotation will subtract from this scale to varying degrees depending on the word (nice = -3, awesome = -5, sweet = -6, cool = -1, etc.).

The only challenge for the entry-level analysis is to print out the sentiment value for an input string.

Advanced-level analysis:

The idea is the same as the entry-level analysis, but instead of finding the overall sentiment value for an input string, you have to find the sentiment value for certain objects. An example:

I really hate ___________________, it is better to spend my money on something I love like _________________.

You will have to attribute the sentiment value to the object in each of those spaces; the first object would have a very "heated" sentiment value, while the second object would have a very "calm" sentiment value.