Browsing by Author "Nakajima, Ai"

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Analyzing Finnish Customer Feedback with Natural Language Processing
(2019-06-17) Kiviniemi-Ghonim, Anastasia
Sähkötekniikan korkeakoulu | Master's thesis
Nowadays companies receive a large number of customer feedback from different channels. Handling customer feedback is essential in terms of maintaining customer satisfaction, listening to the needs and suggestions, and establishing a closer relationship. Natural Language Processing is a rapidly growing field that provides methods for processing and analyzing unstructured textual data. One of the methods for analyzing the semantics of the text is called sentiment analysis. The main task of sentiment analysis is to identify the polarity of the text. This thesis is focused on building a sentiment analysis algorithm for Finnish language using different tools required for text processing steps. The aim was to analyze customer feedback of Telia Finland customers, study the correlation between star ratings and corresponding written comments and perform entity analysis in order to examine a reason for negative reviews within disconnection orders. In addition, the performance of sentiment analysis algorithm was evaluated, and it was estimated that it’s accurate enough for intended usage. The results showed a positive moderate correlation between customer ratings and sentiment scores. Neutrally scored reviews resulted to be majorly negative. Entity analysis showed that most of the complaints within channel package disconnection were associated with the difficulty of provided service and termination of free channel packages.
Layout as a Service (LaaS): A Service Platform for Self-Optimizing Web Layouts
(2020-01-01) Laine, Markku; Nakajima, Ai; Dayama, Niraj; Oulasvirta, Antti
A4 Artikkeli konferenssijulkaisussa
To personalize a web page, case-specific rules or templates must be specified that define the visuospatial layout of elements as well as device-specific adaptation rules for an individual. This approach scales poorly. We present LaaS, a service platform for self-optimizing web layouts to improve their usability at individual, group, and population levels. No hand-coded rules or templates are needed, as LaaS uses combinatorial optimization to generate web layouts for stated design objectives. This allows personalization to be controlled via intuitive objectives that affect the full web layout. We present an extensible architecture and solutions for (1) layout generation using integer programming, (2) data abstractions to mediate between browsers and layout generators, and (3) page restructuring. Moreover, we show how LaaS can be easily deployed as part of existing web pages. Results demonstrate that our approach can produce usable personalized web layouts in diverse scenarios.