Demos

We provide several free resources in the downloads section. The latest version of SenticNet is also available as a Python package and as a free API in many different languages. We also share some code on our GitHub account. Our most advanced tools, however, are only available on our corporate website. Here, you can try three demos of such tools for the sentiment analysis tasks of:
concept extraction
polarity detection
aspect extraction
A simple Twitter data visualization tool (Sentic Tweety) is also available here:






CONCEPT EXTRACTION

Before polarity detection can be performed, multiword expressions need to be extracted from text. Below is a demo of the concept parser, which quickly identifies commonsense concepts from free text without requiring time-consuming phrase structure analysis. From a sentence like “I went to the market to buy fruits and vegetables”, the parser extracts concepts such as go_to_market, market, buy_fruit, and buy_vegetable. The parser makes use of linguistic patterns to deconstruct natural language text into meaningful pairs, e.g., ADJ+NOUN, VERB+NOUN, and NOUN+NOUN, and then exploits commonsense knowledge to infer which of such pairs are more relevant in the current context. In this demo, the output is limited to 15 concepts.


POLARITY DETECTION sentic activation

Polarity detection is the most basic task of sentiment analysis and consists in the binary classification of text as either positive or negative. The demo leverages on linguistic patterns and relies on machine learning in case no patterns are matched. Please note that the task of subjectivity detection is not addressed here. Hence, the demo assumes that the input sentence is opinionated (not neutral). Also, the current version of the demo does not deal with comparative sentences such as "I love iPhone but Android is so much better".


ASPECT EXTRACTION

Aspect extraction is a necessary pre-processing step for aspect-based sentiment analysis, i.e., the detection of polarity with respect to different product or service features (aspects) in stead of the overall polarity of the opinion. This is key for correctly calculating the polarity of sentences in which antithetic opinions about different aspects of the same product are expressed. From a sentence like “the touchscreen is good but the battery lasts very little”, for example, the aspect parser extracts touchscreen and battery.