A Proposed Solution for Sentiment Analysis on Tweets to Extract Emotions from Ambiguous Statements
Abstract : With the advancementofweb technology and its growth, there is huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideasandsharing opinions. Social networkingsiteslike Twitter, Face book, Google are rapidly gaining popularity as they allow people to share and express their views about topics, have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data.Thissurvey focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases. In this paper, we provide a survey and comparative analyses of existing techniques for opinion mininglikemachine learning and lexicon-based approaches, together with evaluation metrics.Usingvarious machine learning algorithms like Naive Bays, Max Entropy, and Support Vector Machine, we provide research on twitter data streams. We have also discussed general challenges and applications of Sentiment Analysis on Twitter.
Sentiment analysis is contextual mining of text which identifies and extracts subjective information in source material, and helping a business to understand the social sentiment of their brand, product or service while monitoring online conversations. However, analysis of social media streams is usually restricted to just basic sentiment analysis and count based metrics. This is akin to just scratching the surface and missing out on those high value insights that are waiting to be discovered. So what should a brand do to capture that low hanging fruit?
• Sentiment analysis tools can identify and analyze many pieces of text automatically and quickly.
• But computer programs have problems recognizing things like sarcasm and irony, negations, jokes, and exaggerations - the sorts of things a person would have little trouble identifying. And failing to recognize these can skew the results.
• 'Disappointed' may be classified as a negative word for the purposes of sentiment analysis, but within the phrase “I wasn't disappointed", it should be classified as positive.
To overcome the drawbacks of the methods we have reviewed above, we propose a new model for sentiment analysis. In this model we combine many techniques to reach our final goal of emotion extraction. The steps for the process are documented below.
1.Retrieval of Data: Public Twitter data is mined using the existing Twitter APIs for data extraction. Tweets would be selected based on a few chosen keywords pertaining to the domain of our concern, i.e. product reviews. We have elected to use the Twitter API due to ease of data extraction.
2.Preprocessing: In this stage, the data is put through a preprocessing stage in which we remove identifying information such as Twitter handles, timestamps of the message and embedded links and videos. Such information is largely irrelevant and may cause false results to be given by our system.
3. Tweet Correction: As tweets are written for human perusal, they often contain slang, misspellings and other irrelevant data. Thus we correct the misspellings in the sentences and look to replace the slang in the sentences with words from standard English that may roughly relate to the slang in question. As slang itself can be used to display a wide variety of sentiment, often with greater emotional impact, this process is necessary so that slang words may be considered as part of the emotion expressed.
4 Polarity detection: In this step we begin the second phase of our proposed system, in which we try to identify the polarity of the sentence in question. If emoticons exist in the statements, they will be used as well to compute the overall polarity of the statement. We aim to find sentences where the polarity detection is not very clear or where the expressed sentiment may be low. We also try to isolate the opinion words in the sentence in relation to a given concept in the sentence.
• Sentiment analysis is a useful tool for any organization or group for which public sentiment or attitude towards them is important for their success - whichever way that success is defined.
• On social media, blogs, and online forums millions of people are busily discussing and reviewing businesses, companies, and organizations. And those opinions are being ‘listened to’ and analyzed.
• Those being discussed are making use of this enormous amount of data by using computer programs that don’t just locate all mentions of their products, services, or business, but also determine the emotions and attitudes behind the words being used.
• The results from sentiment analysis help businesses understand the conversations and discussions taking place about them, and help them react and take action accordingly.
• They can quickly identify any negative sentiments being expressed, and turn poor customer experiences into very good ones.
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