Sentiment Analysis: Can You Tell Me Which Sony Camera Is Good?
I. Introduction Opinions are important to all humans as they influence ones behaviour. In today’s competitive world, businesses and organizations always want to find consumer or public opinions about their products and services. Consumers also want to know the opinions of existing users of a product before purchasing it. In the past, when an individual needed opinions, he/she asked friends and family. When an organization or a business needed public or consumer opinions, it conducted surveys, opinion polls, and focus groups.[1]
With the explosive growth of social media (e.g., reviews sites, forum discussions, blogs, micro-blogs, Twitter, comments, and postings in social network sites) on the Web, individuals and organizations are increasingly …show more content…
B. Issues in Sentiment Analysis
Before exploring Sentiment Analysis in depth we need to understand following issues in Sentiment analysis:
1) A sentence containing sentiment words may not express any sentiment., e.g., “Can you tell me which Sony camera is good?” such sentence contain the sentiment word “good”, but neither expresses a positive or negative opinion on any specific camera.
2) A positive or negative sentiment word may have opposite orientations in different application domains. For example, “suck” usually indicates negative sentiment, e.g., “This camera sucks,” but it can also imply positive sentiment, e.g., “This vacuum cleaner really sucks.”
3) A sentence containing sentiment words may not express any sentiment., e.g., “Can you tell me which Sony camera is good?”
4) Sarcastic sentences with or without sentiment words are hard to deal with, e.g., “What a great car! It stopped working in two days.”
5) Many sentences without sentiment words can also imply opinions. For example The sentence “This washer uses a lot of water” implies a negative sentiment about the washer since it uses a lot of resource …show more content…
Lexican Based Approach
Lexican based approaches for sentiment classifications are based on the insight that the polarity of a piece of text can be obtained on the ground of the polarity of the words which compose it.
1) Dictionary Based Approach:
In this approach first of all a small set of sentiment words which are known as seed words are collected manually with their known positive or negative orientations. Then this set is grown by searching their synonyms and antonyms in WordNet or another online dictionary. The new words are added to the existing seed list. Then next iteration is started. The iteration should be stopped when no new words are found. [4]
V. EVALUATION METRICS
The performance of different methods used for opinion mining is evaluated by calculating various metrics like precision, recall and F-measure.
1) Precision is the fraction of retrieved instances that are relevant.
2) Recall is the fraction of relevant instances that are retrieved.
3) The two measures are sometimes used together in the F1 score (also F-score or F-measure) is a measure of a test's
With the explosive growth of social media (e.g., reviews sites, forum discussions, blogs, micro-blogs, Twitter, comments, and postings in social network sites) on the Web, individuals and organizations are increasingly …show more content…
B. Issues in Sentiment Analysis
Before exploring Sentiment Analysis in depth we need to understand following issues in Sentiment analysis:
1) A sentence containing sentiment words may not express any sentiment., e.g., “Can you tell me which Sony camera is good?” such sentence contain the sentiment word “good”, but neither expresses a positive or negative opinion on any specific camera.
2) A positive or negative sentiment word may have opposite orientations in different application domains. For example, “suck” usually indicates negative sentiment, e.g., “This camera sucks,” but it can also imply positive sentiment, e.g., “This vacuum cleaner really sucks.”
3) A sentence containing sentiment words may not express any sentiment., e.g., “Can you tell me which Sony camera is good?”
4) Sarcastic sentences with or without sentiment words are hard to deal with, e.g., “What a great car! It stopped working in two days.”
5) Many sentences without sentiment words can also imply opinions. For example The sentence “This washer uses a lot of water” implies a negative sentiment about the washer since it uses a lot of resource …show more content…
Lexican Based Approach
Lexican based approaches for sentiment classifications are based on the insight that the polarity of a piece of text can be obtained on the ground of the polarity of the words which compose it.
1) Dictionary Based Approach:
In this approach first of all a small set of sentiment words which are known as seed words are collected manually with their known positive or negative orientations. Then this set is grown by searching their synonyms and antonyms in WordNet or another online dictionary. The new words are added to the existing seed list. Then next iteration is started. The iteration should be stopped when no new words are found. [4]
V. EVALUATION METRICS
The performance of different methods used for opinion mining is evaluated by calculating various metrics like precision, recall and F-measure.
1) Precision is the fraction of retrieved instances that are relevant.
2) Recall is the fraction of relevant instances that are retrieved.
3) The two measures are sometimes used together in the F1 score (also F-score or F-measure) is a measure of a test's