Average valence by imdb movie review rating.

Assessing whether texts are positive or negative—sentiment analysis—has wide-ranging applications across many disciplines. Automated approaches make it possible to code near unlimited quantities of texts rapidly, replicably, and with high accuracy. Compared to machine learning and large language model (LLM) approaches, lexicon-based methods may sacrifice some in performance, but in exchange they provide generalizability and domain independence, while crucially offering the possibility of identifying gradations in sentiment. We demonstrate the strong performance of lexica using MultiLexScaled, an approach which averages valences across a number of widely-used general-purpose lexica. We validate it against benchmark datasets from a range of different domains, comparing performance against machine learning and LLM alternatives. In addition, we illustrate the value of identifying fine-grained sentiment levels by showing, in an analysis of pre- and post-9/11 British press coverage of Muslims, that binarized valence metrics give rise to different (and erroneous) conclusions about the nature of the post-9/11 shock as well as about differences between broadsheet and tabloid coverage. The code to apply MultiLexScaled is available online.旨在判断文本情感正负属性的情感分析（sentiment analysis），在众多学科领域均拥有广泛的应用前景。自动化分析方法可实现对近乎无限量级的文本进行快速、可复现且高精度的编码标注工作。相较于机器学习与大语言模型（LLM）方法，基于词典的情感分析方法虽会在性能上有所妥协，但却具备更强的泛化性与领域无关性，尤为关键的是，其能够实现情感细微层级的识别。本研究通过MultiLexScaled方法验证了情感词典的优异性能：该方法对多款广泛使用的通用情感词典的情感效价分值取平均值。我们针对多个不同领域的基准数据集对该方法进行性能验证，并将其与机器学习及LLM同类方法进行对比。此外，通过对9·11事件前后英国媒体对穆斯林群体的报道展开分析，我们展示了识别细粒度情感层级的重要价值：研究发现，采用二值化情感效价指标会得出关于9·11事件冲击本质以及严肃大报与小报报道差异的不同甚至错误的结论。MultiLexScaled方法的应用代码可通过线上渠道获取。