The Opinosis REST API is available to all academic researchers. You can use a command line tool like cURL to access the API or you can also easily access the API from any programming language using HTTP request and response libraries.
Opinion Driven Decision Support is a term that I coined as part of my Ph.D. thesis refering to the use of large amounts of online opinions to facilitate business and consumer decision making. The idea in my thesis is to combine the strengths of search technologies with opinion mining and analysis tools to provide a powerful decision making platform.
This paper presents a new unsupervised approach to generating ultra-concise summaries of opinions. We formulate the problem of generating such a micropinion summary as an optimization problem, where we seek a set of concise and non-redundant phrases that are readable and represent key opinions in text. We measure representativeness based on a modifi ed mutual information function and model readability with an n-gram language model. We propose some heuristic algorithms to efficiently solve this optimization problem.
The Opinosis Summarizer Software is a demo version of a summarizer that generates concise abstractive summaries of highly redundant text. It was primarily used to summarize opinions, and thus it can be regarded as a opinion summarization software.
We present a novel graph-based summarization framework (Opinosis) that generates concise abstractive summaries of highly redundant opinions. Evaluation results on summarizing user reviews show that Opinosis summaries have better agreement with human summaries compared to the baseline extractive method. The summaries are readable, reasonably well-formed and are informative enough to convey the major opinions.