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Introduction
Sentiment Analysis has been more than just a social analytic tool. It’s been an interesting field of study. But it is a field that is still being studied, although not at great lengths due to the intricacy of this analysis. That is this field has functions that are too complicated for machines to understand. The ability to understand sarcasm, hyperbole, positive feelings, or negative feelings has been difficult, for machines that lack feelings. Algorithms have not been able to predict with more than 60% accuracy the feelings portrayed by people. Yet with so many limitations this is one field which is growing at great pace within many industries. Companies want to accommodate the sentiment analysis tools into areas of customer feedback, marketing, CRM, and ecommerce.
Way Ahead
Sentiment analysis methods till now have been used to detect the polarity in the thoughts and opinions of all the users that access social media. Researchers and Businesses are very interested to understand the thoughts of people and how they respond to everything happening around them. Companies use this to evaluate their advertisement campaigns and to improve their products.
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There is too much potential in machine learning, overtaking some of the manual labor of some lexicon based tasks that are labor intensive. For example, lexicon sentiment creation is labor intensive and there are already unsupervised methods to create them. This is where machine learning will play a crucial role. Such algorithms will also have to understand and analyze natural text concept-wise and context-wise. Time will also be a crucial element looking at the amount of data that is being generated on the Web today. Collecting opinions on the web will still requires processing that can filter out un-opinionated user-generated content and also to test the trustworthiness of the opinion and its source.
There is a lot of scope in analyzing the video and images on the web. Now a days, with the advent of Facebook, Instagram and Video vines people are expressing their thoughts with pictures and videos along with text. Sentiment analysis will have to pace up with this change. Tools which are helping companies to change strategies based on Facebook and Twitter will also have to accommodate the number of likes and re-tweets that the thought is generating on the Social media. People follow and unfollow people and comments on Social Media but never comment so there is scope in analyzing these aspects of the Web as well.
The use of punctuation is an obstacle in Sentiment Analysis which is under research as well. Sentiment Analysis has started helping us to predict events just like in the case of Obama vs Romney but is still naïve in most cases. A sentiment analysis tool Tweview had predicted the winner of the show X factor but eventually that person came second. So improvements on the analysis is one scope which is under way by many tools available on the web.
As new text types appear on the Social Web, the techniques to pre-process, as well as to tackle their informal style must be adapted, so as to obtain acceptable levels of performance of the sentiment analysis systems. The field will have to combine with effective computing, psychology and neuroscience to converge on a unified approach to understanding the sentiments better.
Roadblocks
Many tools and algorithms rely on the polarity of the words and the scoring is dependent on this polarity. This means that accuracy drops since the semantics of the complete sentence is lost. The semantics of the sentence makes it difficult to measure the polarity of the sentences on individual words. For eg. “This car is anything but useful”. The word useful can make this sentence positive but eventually this is a negative sentence overall. There are a few limitation to sentiment analysis which are hampering the progress of the accuracy of the models.
The positive or negative word might mean completely opposite depending upon the context used in the sentence. For example “My car is very good at using up the petrol at a faster rate.” Then sometimes the sentence ambiguity can be a problem since some positive or negative words might mean nothing in perspective of the sentence and sometimes words with no individual meaning express a lot of sentiment in the sentence. Sarcasm is the biggest challenge that sentiment analysis faces. Machine or algorithms with no emotion will find it extremely difficult to differentiate when users are commenting sarcastically.
The language used throughout social media is different. Financial industry have their own language which means completely differs from Entertainment industry. This makes it hard for nay tool to predict the emotion or semantic of the sentence. People also use a lot of slang language and hashtags which makes the accuracy of the algorithms lower. It is difficult for the tool to even understand who the object of the sentence is. For example “I feel the browser is working fine but my friend hates working on it”.
Sustenance
Sentiment analysis is not all that smooth after all. There are several issues related to Sentiment analysis that could lead to the loss of popularity of the technique.
- Opinion spam: Sentiment analysis can be used by competitors to portray negative image of a company. Once sentiment analysis gains popularity as a metric to gauge performance and brand image of a company, such mal-practices may become very common which will lead to decreased popularity of Sentiment Analysis.
- Result measure: The outputs of Sentiment analysis are useful as a reactive measure. It cannot be used to predict the performance of a company or other metrics. In some cases, Sentiment analysis can be redundant and can be only a reporting measure after the damage has been done.
- Lack of complete information. Biased results based on the sources: The sources of extracting information can be a major roadblock in sentiment analysis. Analysis of a scenario on incomplete information can lead to skewed results. Sources like Twitter, Facebook can be mined to get complete information.
But, other sources like blogs, posts, forums etc can be difficult to retrieve information from that can lead to a biased result-set.
Conclusion
Despite all the challenges and potential problems that threatens Sentiment analysis, one cannot ignore the value that it adds to the industry. Because Sentiment analysis bases its results on factors that are so inherently humane, it is bound to become one the major drivers of many business decisions in future. Improved accuracy and consistency in text mining techniques can help overcome some current problems faced in Sentiment analysis. Looking ahead, what we can see is a true social democracy that will be created using Sentiment analysis, where we can harness the wisdom of the crowd rather than a select few “experts”. A democracy where every opinion counts and every sentiment affects decision making.
References:
http://www.scoop.it/t/social-media-monitoring-tools-and-solutions 1st picture.
http://www.saama.com/sentiment-analytics-the-gold-mine-which-you-didn-t-mine/ 3rd picture
http://www.brandwatch.com/2013/12/social-data-gets-the-x-factor/ Tweview