## 4 Results and Discussion
### Results of Sentiment Lexicon from Hotel Reviews  
According to the process given in Section 3.3, the PolarityRank algorithm is employed to create a sentiment lexicon from the corpus composed of all textual reviews after preprocessing. Based on the selecting criteria, the nouns, adjectives, verbs and adverbs with POS are selected as candidate sentiment words. To ensure the efficiency of the PolarityRank algorithm, the final list of candidate sentiment words is composed of words that exist in at least 30 reviews. A total of 13,933 candidate sentiment words and the co-occurrence \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Kano Category**} & \multicolumn{4}{c}{**IPA Strategy**} \\ \cline{2-5}  & **Concentrate** & **Low Priority** & **Keep Up the Good Work** & **Possible Overkill** \\ \hline Basic & 1 & 4 & 7 & 10 \\ Performance & 2 & 5 & 8 & 11 \\ Excitement & 3 & 6 & 9 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The attribute priority rankings for resource allocation. frequency of any two nodes in the whole dataset are obtained. Subsequently, the initial positive and negative sentiment values of each candidate word are assigned based on SentiWordNet. The results of candidate sentiment words with POS, frequency and initial sentiment value are shown in Table 5. In Table 5, \"Tag\" indicates the POS of each candidate sentiment words, and \"Number of Words\" indicates the number of times that candidate sentiment words appear in the whole corpus. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Value** & \multicolumn{3}{c}{**Location**} & \multicolumn{3}{c}{**Service**} \\ \hline
**Sub-Attributes** & **Similarity** & **Sub-Attributes** & **Similarity** & **Sub-Attributes** & **Similarity** \\ \hline represent & 0.6609 & position & 0.7390 & middling & 0.6122 \\ competitively & 0.6373 & localisation & 0.6764 & exemplar & 0.6041 \\ comparative & 0.6291 & narrow & 0.6467 & fatless & 0.5972 \\ money & 0.6283 & fatherland & 0.6444 & outstanding & 0.5942 \\ ratio & 0.6070 & breckenridge & 0.6380 & approachability & 0.5908 \\ introductory & 0.6064 & geest & 0.6367 & topnotch & 0.5858 \\ comparably & 0.6055 & truistic & 0.6341 & cleanness & 0.5787 \\ reasonable & 0.5948 & locality & 0.6308 & tentativeness & 0.5771 \\ affordability & 0.5733 & heartland & 0.6254 & exceptional & 0.5751 \\ inline & 0.5720 & kenton & 0.6245 & staff & 0.5668 \\ \hline
**Room** & \multicolumn{3}{c}{**Cleanliness**} & \multicolumn{3}{c}{**Sleep Quality**} \\ \hline
**Sub-Attributes** & **Similarity** & **Sub-Attributes** & **Similarity** & **Sub-Attributes** & **Similarity** \\ \hline plushly & 0.6636 & spotless & 0.7262 & soundly & 0.8287 \\ suite & 0.6609 & immaculate & 0.6335 & undisturbed & 0.7309 \\ uprate & 0.6463 & tidy & 0.6275 & hypnos & 0.6987 \\ handspring & 0.6425 & spacey & 0.6169 & insomnia & 0.6950 \\ furbished & 0.6340 & furbished & 0.6079 & restless & 0.6841 \\ cubit & 0.6321 & conformable & 0.6057 & soundproofed & 0.6737 \\ spacious & 0.6289 & appoint & 0.6056 & silent & 0.6722 \\ pristinely & 0.6284 & spacious & 0.6055 & pillow & 0.6691 \\ spacey & 0.6214 & equipped & 0.5960 & uninterrupted & 0.6646 \\ luminous & 0.6207 & scrupulously & 0.5927 & blackout & 0.6643 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Top 10 similar sub-attributes with respect to the six attributes from five-star hotels.