Many of the existing sentiment analysis techniques are based on supervised
learning, and they demand the availability of valuable training datasets to
train their models. When dataset freshness is critical, the annotating of high
speed unlabelled data streams becomes critical but remains an open problem. In
this paper, we propose PLStream, a novel Apache Flink-based framework for fast
polarity labelling of massive data streams, like Twitter tweets or online
product reviews. We address the associated implementation challenges and
propose a list of techniques including both algorithmic improvements and system
optimizations. A thorough empirical validation with two real-world workloads
demonstrates that PLStream is able to generate high quality labels (almost 80%
accuracy) in the presence of high-speed continuous unlabelled data streams
(almost 16,000 tuples/sec) without any manual efforts.

本文提出了基于 Apache Flink 的 PLStream 框架，通过算法和系统优化处理数据流中的高速未标注数据，成功实现了高质量标注（准确率近 80%）且不需要人工干预的连续未标注数据流（约 16,000 元组 / 秒）。

一种用于快速极性标记大数据流的框架

A Framework for Fast Polarity Labelling of Massive Data Streams

We introduce a novel logic-based system for reasoning over data streams,
which relies on a framework enabling a tight, fine-tuned interaction between
Apache Flink and the I^2-DLV system. The architecture allows to take advantage
from both the powerful distributed stream processing capabilities of Flink and
the incremental reasoning capabilities of I^2-DLV based on overgrounding
techniques. Besides the system architecture, we illustrate the supported input
language and its modeling capabilities, and discuss the results of an
experimental activity aimed at assessing the viability of the approach. This
paper is under consideration in Theory and Practice of Logic Programming
(TPLP).

基于逻辑的数据流推理系统，利用 Apache Flink 和 I^2-DLV 系统的框架实现了紧密微调的交互。