efficient Syntactic Analysis for Large-scale Sentiment Analysis

Introduction

One of the key aspects of any successful business is knowing how customers feel about its brand and products. For this purpose, sentiment analysis or opinion mining tools could be paramount in helping companies to succeed.

Current Limitations

However, the current state of the art sentiment analysis solutions (both commercial and academic) present important drawbacks including:

• Low accuracy
• Low performance (response time around 100-1000 ms)
• High computational cost
• High price (around 500/month on average)

These limitations relegate these solutions to consolidated big brands, social listening agencies, or consulting firms offering social listening services.

Project Overview

SALSA aims to democratize the analysis and transformation of internet/social data into knowledge creation for decision-makers, making large-scale sentiment analysis technology viable for small entities without massive computational power.

Technological Innovations

SALSA will explore the potential of the powerful models and algorithms developed within ERC Starting Grant FASTPARSE to create the first AI-based syntax-guided sentiment analysis engine which is:

a) Accurate: It uses syntactic information to infer the opinions contained in each sentence from its structure and the relationship between its words, rather than shallow methods that consider words in isolation.

b) Cost-effective: It employs fast parsers that have a throughput in the order of 1000 sentences per second on consumer-grade hardware, and that can work without time- and memory-hungry large language models.

Business Model

SALSA will follow an open-source software business model in which we will explore several sources of revenue, most based on service-level agreements. This will highly contribute to the competitiveness of the EU technological market by reducing their dependency on the oligopoly of technological giants (mostly American and Chinese) that currently have a dominant position in language technologies, largely thanks to their enormous computational resources.