Leverage Sentiment

The action of a lever or the mechanical advantage gained by it. Power , effectiveness trying to gain more political leverage. The use of credit to enhance one's speculative capacity. To provide (something, such as a corporation) or supplement (something, such as money) with leverage also to enhance as if by supplying with financial leverage. To use for gain exploit shamelessly leverage the system to their advantage

The emergence of new social media such as blogs, message boards, news, and Web content in general has dramatically changed the ecosystems of corporations. Consumers, non-profit organizations, and other forms of communities are extremely vocal about their opinions and perceptions on companies and their brands on the Web. The ability to leverage such "voice of the Web" to gain consumer, brand, and market insights can be truly differentiating and valuable to todaypsilas corporations. In particular, one important form of insights can be derived from sentiment analysis on Web content. Sentiment analysis traditionally emphasizes on classification of Web comments into positive, neutral, and negative categories. This paper goes beyond sentiment classification by focusing on techniques that could detect the topics that are highly correlated with the positive and negative opinions. Such techniques, when coupled with sentiment classification, can help the business analysts to understand both the overall sentiment scope as well as the drivers behind the sentiment. In this paper, we describe our overall sentiment analysis system that consists of such sentiment analysis techniques. We then detail a novel topic detection method using point-wise mutual information and term frequency distribution. We demonstrate the effectiveness of our overall approaches via several case studies on different social media data sets.

Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches. Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored. Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable "affinity" to particular emotions. Statistical methods leverage elements from machine learning such as latent semantic analysis, support vector machines, "bag of words", "Pointwise Mutual Information" for Semantic Orientation, and deep learning. More sophisticated methods try to detect the holder of a sentiment (i.e., the person who maintains that affective state) and the target (i.e., the entity about which the affect is felt). To mine the opinion in context and get the feature about which the speaker has opined, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text. Hybrid approaches leverage both machine learning and elements from knowledge representation such as ontologies and semantic networks in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so.

Open source software tools as well as range of free and paid sentiment analysis tools deploy machine learning, statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media. Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts. The system can help perform affective commonsense reasoning. Sentiment analysis can also be performed on visual content, i.e., images and videos (see Multimodal sentiment analysis). One of the first approaches in this direction is SentiBank utilizing an adjective noun pair representation of visual content. In addition, the vast majority of sentiment classification approaches rely on the bag-of-words model, which disregards context, grammar and even word order. Approaches that analyses the sentiment based on how words compose the meaning of longer phrases have shown better result, but they incur an additional annotation overhead.

A human analysis component is required in sentiment analysis, as automated systems are not able to analyze historical tendencies of the individual commenter, or the platform and are often classified incorrectly in their expressed sentiment. Automation impacts approximately 23% of comments that are correctly classified by humans. However, humans often disagree, and it is argued that the inter-human agreement provides an upper bound that automated sentiment classifiers can eventually reach.

The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. This is usually measured by variant measures based on precision and recall over the two target categories of negative and positive texts. However, according to research human raters typically only agree about 80% of the time (see Inter-rater reliability). Thus, a program that achieves 70% accuracy in classifying sentiment is doing nearly as well as humans, even though such accuracy may not sound impressive. If a program were "right" 100% of the time, humans would still disagree with it about 20% of the time, since they disagree that much about any answer.

On the other hand, computer systems will make very different errors than human assessors, and thus the figures are not entirely comparable. For instance, a computer system will have trouble with negations, exaggerations, jokes, or sarcasm, which typically are easy to handle for a human reader: some errors a computer system makes will seem overly naive to a human. In general, the utility for practical commercial tasks of sentiment analysis as it is defined in academic research has been called into question, mostly since the simple one-dimensional model of sentiment from negative to positive yields rather little actionable information for a client worrying about the effect of public discourse on e.g. brand or corporate reputation.

To better fit market needs, evaluation of sentiment analysis has moved to more task-based measures, formulated together with representatives from PR agencies and market research professionals. The focus in e.g. the RepLab evaluation data set is less on the content of the text under consideration and more on the effect of the text in question on brand reputation.

Because evaluation of sentiment analysis is becoming more and more task based, each implementation needs a separate training model to get a more accurate representation of sentiment for a given data set.

Sentiment Analysis Sentiment Analysis is the most common text classification tool that analyses an incoming message and tells whether the underlying sentiment is positive, negative our neutral. You can input a sentence of your choice and gauge the underlying sentiment by playing with the demo.

Social media sentiment analysis is a pretty familiar term by now. It has its roots back in the early 2000s and has been making its way through the businesses ever since. The majority (70%) of the B2C companies analyze customer feedback and product reviews on social media to deliver the best in class services. In the previous blog, you read about the applications of sentiment analysis in business. You must be fairly aware of why you need it for your business. Now that you have the access to it, the real chase begins. This blog deals with what you can and you should do with the insights generated when you run sentiment analysis on social media.

We learned about Leverage Sentiment Analysis.