Purpose The purpose of this paper is to establish a data mining model for performing sentiment analysis on open-ended qualitative LibQUAL+ comments, providing a further method for year-to-year comparison of user satisfaction, both of the library as a whole and individual topics. Design/methodology/approach A training set of 514 comments, selected at random from five LibQUAL+ survey responses, was manually reviewed and labeled as having a positive or negative sentiment. Using the open-source RapidMiner data mining platform, those comments provided the framework for creating library-specific positive and negative word vectors to power the sentiment analysis model. A further process was created to help isolate individual topics within the larger comments, allowing for more nuanced sentiment analysis. Findings Applied to LibQUAL+ comments for a Canadian mid-sized academic research library, the model suggested a fairly even distribution of positive and negative sentiment in overall comments. When filtering comments into affect of service, information control and library as place, the three dimensions’ relative polarity mirrored the results of the quantitative LibQUAL+ questions, with highest scores for affect of service and lowest for library as place. Practical implications The sentiment analysis model provides a complementary tool to the LibQUAL+ quantitative results, allowing for simple, time-efficient, year-to-year analysis of open-ended comments. Furthermore, the process provides the means to isolate specific topics based on specified keywords, allowing individual institutions to tailor results for more in-depth analysis. Originality/value To best account for library-specific terminology and phrasing, the sentiment model was created using LibQUAL+ open-ended comments as the foundation for the sentiment model’s classification process. The process also allows individual topics, chosen to meet individual library needs, to be isolated and independently analyzed, providing more precise examination.

中文翻译：

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为LibQUAL +评论构建情感分析模型

目的本文的目的是建立一个数据挖掘模型，用于对开放式定性LibQUAL +注释进行情感分析，为图书馆整体和单个主题的用户满意度逐年比较提供另一种方法。 。设计/方法/方法从五个LibQUAL +调查答复中随机选择的514条评论训练集进行了人工检查，并被标记为具有正面或负面的情绪。通过使用开源RapidMiner数据挖掘平台，这些评论提供了创建特定于库的正负向量词以增强情感分析模型的框架。创建了一个进一步的过程，以帮助将较大的注释中的各个主题隔离开来，从而进行更细微的情感分析。该发现适用于加拿大一家中型学术研究图书馆的LibQUAL +评论，该模型表明总体评论中积极情绪和消极情绪的分布相当平均。在将评论过滤到服务，信息控制和图书馆作为场所的影响中时，这三个维度的相对极性反映了定量LibQUAL +问题的结果，服务影响的得分最高，图书馆作为场所的得分最低。实际意义情绪分析模型为LibQUAL +定量结果提供了一种补充工具，可以对开放式评论进行简单，高效且逐年的分析。此外，该过程提供了根据指定的关键字隔离特定主题的方法，从而允许各个机构定制结果以进行更深入的分析。独创性/价值为了更好地说明图书馆特定的术语和措辞，使用LibQUAL +开放式注释作为情感模型分类过程的基础，创建了情感模型。该过程还允许选择满足单个图书馆需求的单个主题，并对其进行独立分析，从而提供更精确的检查。