With the widespread use of social networks, detecting the topics discussed in
these networks has become a significant challenge. The current works are mainly
based on frequent pattern mining or semantic relations, and the language
structure is not considered. The meaning of language structural methods is to
discover the relationship between words and how humans understand them.
Therefore, this paper uses the Concept of the Imitation of the Mental Ability
of Word Association to propose a topic detection framework in social networks.
This framework is based on the Human Word Association method. The performance
of this method is evaluated on the FA-CUP dataset. It is a benchmark dataset in
the field of topic detection. The results show that the proposed method is a
good improvement compared to other methods, based on the Topic-recall and the
keyword F1 measure. Also, most of the previous works in the field of topic
detection are limited to the English language, and the Persian language,
especially microblogs written in this language, is considered a low-resource
language. Therefore, a data set of Telegram posts in the Farsi language has
been collected. Applying the proposed method to this dataset also shows that
this method works better than other topic detection methods.

本论文提出一种基于 Word Association 的社交网络话题检测框架，利用语言结构方法发现单词间的关系，结合人类 Word Association 模型实现更好的效果，在英语和波斯语低资源语言中进行测试，显示出良好的性能。

一种基于人类词语联想的社交网络主题检测模型

A Human Word Association based model for topic detection in social networks

Much of information sits in an unprecedented amount of text data. Managing
allocation of these large scale text data is an important problem for many
areas. Topic modeling performs well in this problem. The traditional generative
models (PLSA,LDA) are the state-of-the-art approaches in topic modeling and
most recent research on topic generation has been focusing on improving or
extending these models. However, results of traditional generative models are
sensitive to the number of topics K, which must be specified manually. The
problem of generating topics from corpus resembles community detection in
networks. Many effective algorithms can automatically detect communities from
networks without a manually specified number of the communities. Inspired by
these algorithms, in this paper, we propose a novel method named Hierarchical
Latent Semantic Mapping (HLSM), which automatically generates topics from
corpus. HLSM calculates the association between each pair of words in the
latent topic space, then constructs a unipartite network of words with this
association and hierarchically generates topics from this network. We apply
HLSM to several document collections and the experimental comparisons against
several state-of-the-art approaches demonstrate the promising performance.

本文提出一种新的名为 Hierarchical Latent Semantic Mapping (HLSM) 的主题生成方法，它可以自动从语料库中生成主题，并使用单词之间的关联和层次生成主题网络。实验表明，相比于现有的一些最先进的方法，HLSM 在几个文档集合上表现良好。

自动主题生成的分层潜在语义映射

Hierarchical Latent Semantic Mapping for Automated Topic Generation

The evaluative character of a word is called its semantic orientation.
Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and
negative semantic orientation indicates criticism (e.g., "disturbing",
"superfluous"). Semantic orientation varies in both direction (positive or
negative) and degree (mild to strong). An automated system for measuring
semantic orientation would have application in text classification, text
filtering, tracking opinions in online discussions, analysis of survey
responses, and automated chat systems (chatbots). This paper introduces a
method for inferring the semantic orientation of a word from its statistical
association with a set of positive and negative paradigm words. Two instances
of this approach are evaluated, based on two different statistical measures of
word association: pointwise mutual information (PMI) and latent semantic
analysis (LSA). The method is experimentally tested with 3,596 words (including
adjectives, adverbs, nouns, and verbs) that have been manually labeled positive
(1,614 words) and negative (1,982 words). The method attains an accuracy of
82.8% on the full test set, but the accuracy rises above 95% when the algorithm
is allowed to abstain from classifying mild words.

该论文介绍了一种从词汇统计学关联性出发推断词汇的语义方向的方法，通过应用点对点互信息（PMI）和潜在语义分析（LSA）两种不同的统计测量方式进行评估，该方法在英语单词集测试中达到 82.8% 的准确性，但当允许算法避免类别划分时，准确性可以提高至 95% 以上。